This notebook runs through the CNS analysis used in the VAE paper (ref: ).
We use genes that were found to be differentially expressed and aim to understand how different groups of genes responded to PRC2 knockout.
We do this by using a variational autoencoder (VAE).
Our VAE model uses a three layer network, and as input data, takes in the normalised expression matrix including the wild type and response to Eed-cKO.
1) Function setup
2) Read in processed dataframe and organise this
3) Build VAE model
4) Setup data for visualisations
5) Quantify separability of latent space
6) Save VAE ranks to csv to run GSEA in R
6) Build gene groups using VAE latent space
7) Save gene groups for ORA in R
8) Run motif anlaysis using FIMO on gene groups
9) Inspecting the groups in context of chromHMM annotations
10) Inspect groups in terms of annotations for Epi paper
"""
--------------------------------------------------------
Imports
--------------------------------------------------------
"""
import os, sys
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import venn
from matplotlib_venn import venn3
from sklearn.decomposition import PCA
from scipy import stats
from sciviso import *
from sciutil import SciUtil
from scivae import Vis as VVis
import string
u = SciUtil()
module_path = os.path.abspath(os.path.join(''))
sys.path.append(module_path)
"""
--------------------------------------------------------
Global variables
--------------------------------------------------------
"""
date = '20210217'
gene_id = 'entrezgene_id'
gene_name = 'external_gene_name'
sci_colour = ['#483873', '#1BD8A6', '#B117B7', '#AAC7E2', '#FFC107', '#016957', '#9785C0',
'#D09139', '#338A03', '#FF69A1', '#5930B1', '#FFE884', '#35B567', '#1E88E5',
'#ACAD60', '#A2FFB4', '#B618F5', '#854A9C']
hist_colour = '#483873'
e11_colour = '#BEC0C2'
e13_colour = '#A7A9AC'
e15_colour = '#7D7E81'
e18_colour = '#58595B'
fb_colour = '#ffbf80'
mb_colour = '#ff8c1a'
hb_colour = '#b35900'
sc_colour = '#663300'
fb_color = '#ffbf80'
mb_color = '#ff8c1a'
hb_color = '#b35900'
sc_color = '#663300'
h3k36me3_colour = '#CEF471'
h3k27me3_colour = '#9F71F4'
h3k4me3_colour = '#9F00FA'
h3k4me2_colour = '#5930B1'
h3k4me1_colour = '#FFE884'
h3k27ac_colour = '#35B567'
h3k9me3_colour = '#1E88E5'
h3k9ac_colour = '#A2FFB4'
wt_colour = '#AADFF1'
ko_colour = '#A53736'
sns.palplot(sci_colour)
sns.color_palette(sci_colour)
project_name = '3_node_significant_genes'
data_dir = '../../data/'
r_dir = f'{data_dir}results/deseq2/'
fig_dir = f'../../figures/{project_name}/vae/'
output_dir = f'{data_dir}results/{project_name}/vae/'
input_dir = f'{data_dir}results/prelim/'
rna_dir = f'{input_dir}feature-counts_04052020/'
supp_dir = f'{data_dir}input/supps/'
logging_dir = 'logging/'
ora_dir = '../../data/results/3_node_significant_genes/functional/'
grey = '#bdbdbd'
hist_cmap = 'Greens'
rna_cmap = 'Purples'
div_cmap = 'seismic'
experiment_name = project_name
from_saved = True # Load from a saved VAE
df_all = pd.read_csv(f'{input_dir}df-all_epi-2500_20210124.csv')
df_training = pd.read_csv(f'{input_dir}df-significant_epi-2500_20210124.csv')
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3062: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False. has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
Here we just initialise the functions that will be used in the notebook.
"""
--------------------------------------------------------
Colour/style getters
--------------------------------------------------------
"""
def get_time_colour(c):
if '11' in c or '10' in c:
return e11_colour
elif '13' in c or '12' in c:
return e13_colour
elif '15' in c or '14' in c:
return e15_colour
elif '18' in c or '16' in c:
return e18_colour
return '#FFFFFF'
def get_tissue_colour(c):
if 'sc' in c or 'spinal' in c:
return sc_colour
elif 'hb' in c or 'hindbrain' in c:
return hb_colour
elif 'fb' in c or 'forebrain' in c:
return fb_colour
elif 'mb' in c or 'midbrain' in c:
return mb_colour
return '#FFFFFF'
def get_cond_colour(c):
if 'ko' in c:
return ko_colour
elif 'wt' in c:
return wt_colour
return '#FFFFFF'
def get_mark_colour(c):
if '36me3' in c:
return h3k36me3_colour
elif '27me3' in c:
return h3k27me3_colour
elif 'K4me3' in c:
return h3k4me3_colour
elif 'K4me2' in c:
return h3k4me2_colour
elif 'K4me1' in c:
return h3k4me1_colour
elif '27ac' in c:
return h3k27ac_colour
elif 'K9me3' in c:
return h3k9me3_colour
elif 'K9ac' in c:
return h3k9ac_colour
return '#FFFFFF'
"""
--------------------------------------------------------
Plotting
--------------------------------------------------------
"""
def pplot():
return
def cplot():
return
def save_fig(title):
plt.savefig(f'{fig_dir}{title.replace(" ", "-")}_{experiment_name}_{date}.svg')
"""
--------------------------------------------------------
Heatmap and vis function
--------------------------------------------------------
"""
def plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes,
input_cols, vae_data, vae, gene_name, mark, method='input',
title="", merge_reps=False, input_col_ids=None, cmap='', genes=None,
values=None):
brain_genes = []
colours = []
gene_idxs = []
genes = genes if genes is not None else df[gene_name].values
i = 0
for g in genes:
if g in fb_genes:
brain_genes.append(g)
colours.append(fb_colour)
gene_idxs.append(i)
i += 1
i = 0
for g in genes:
if g in mb_genes:
brain_genes.append(g)
colours.append(mb_colour)
gene_idxs.append(i)
i += 1
i = 0
for g in genes:
if g in hb_genes:
brain_genes.append(g)
colours.append(hb_colour)
gene_idxs.append(i)
i += 1
i = 0
for g in genes:
if g in sc_genes:
brain_genes.append(g)
colours.append(sc_colour)
gene_idxs.append(i)
i += 1
heatmap_df = pd.DataFrame()
heatmap_df[gene_name] = brain_genes
if method == 'decoding':
deocded = vae.decoder.predict(vae_data)
deocded = scaler.fit_transform(deocded)
vals = deocded[gene_idxs]
elif method == 'input':
vals = df[input_cols].values[gene_idxs]
else:
vals = vae_data[gene_idxs]
vals = values[gene_idxs] if values is not None else vals
vals = np.nan_to_num(vals)
min_r = np.min(vals, axis = 1)
max_r = np.max(vals, axis = 1)
ov_c = max_r - min_r
if not merge_reps:
cols = []
for i, c in enumerate(input_cols):
if input_col_ids is not None:
if i in input_col_ids:
heatmap_df[c] = np.nan_to_num(vals[:,i])
cols.append(c)
else:
heatmap_df[c] = np.nan_to_num(vals[:,i])
cols.append(c)
heatmap_cols = cols
else:
i = 0
offset = 0
max_c = len(input_cols)
if input_col_ids is not None:
offset = input_col_ids[0]
max_c = len(input_col_ids)
heatmap_cols = []
while(i < max_c):
if input_col_ids is not None:
heatmap_df[input_cols[input_col_ids[i]]] = (vals[:, input_col_ids[i]] - min_r)/(max_r - min_r)
heatmap_cols.append(input_cols[input_col_ids[i]])
else:
heatmap_df[input_cols[i]] = (vals[:, i + offset] - min_r)/(max_r - min_r)
heatmap_cols.append(input_cols[i])
i += 1
print(heatmap_df.head())
cols = []
col_colours = []
cond_colours = []
tissue_colours = []
time_colours = []
for c in heatmap_df.columns:
if 'wt' in c or 'ko' in c:
cols.append(c)
time_colours.append(get_time_colour(c))
tissue_colours.append(get_tissue_colour(c))
cond_colours.append(get_cond_colour(c))
if mark in c and hist_metric in c:
time_colours.append(get_time_colour(c))
tissue_colours.append(get_tissue_colour(c))
cond_colours.append('white')
cols.append(c)
elif 'log2' in c:
time_colours.append(get_time_colour(c))
tissue_colours.append(get_tissue_colour(c))
cond_colours.append(get_mark_colour(c))
cols.append(c)
col_colours = [cond_colours, tissue_colours, time_colours]
# Now plot the rnaseq response as a heatmap
vmin = None
vmax = None
if cmap == hist_cmap or cmap == rna_cmap:
vmin = 0
vmax = 10
elif cmap == div_cmap:
vmin = -3
vmax = 3
heatmap = Heatmap(heatmap_df, heatmap_cols, gene_name,
title=title, cluster_cols=False, cluster_rows=False, vmin=vmin, vmax=vmax,
figsize=(5, 5),
cmap=cmap)
heatmap.cmap = cmap
heatmap.plot()
pplot()
save_fig(title)
plt.show()
Here we import the results from running the AP-axis dataset generation script.
This used the output from differential analysis and also annotated histone marks to genes.
# Smooth out the columns in the data frame i.e. for the clones we only put in the mean of the two replicates
cols_to_merge = [c for c in df_training.columns if 'wt' in c or 'ko' in c]
col_names = []
col_values = []
values = df_training[cols_to_merge].values
i = 0
while(i < len(cols_to_merge)):
df_training[f'{cols_to_merge[i][:-1]}_merged-rep'] = 0.5 * (df_training[cols_to_merge[i]].values +
df_training[cols_to_merge[i + 1]].values)
i += 2
cols_to_merge = [c for c in df_all.columns if 'wt' in c or 'ko' in c]
col_names = []
col_values = []
values = df_all[cols_to_merge].values
i = 0
while(i < len(cols_to_merge)):
df_all[f'{cols_to_merge[i][:-1]}_merged-rep'] = 0.5 * (df_all[cols_to_merge[i]].values +
df_all[cols_to_merge[i + 1]].values)
i += 2
from sklearn import linear_model
plt.rcParams['svg.fonttype'] = 'none' # Ensure text is saved as text
plt.rcParams['figure.figsize'] = (1, 1)
sns.set(rc={'figure.figsize': (1, 1), 'font.family': 'sans-serif',
'font.sans-serif': 'Arial', 'font.size': 6}, style='ticks')
def set_ax_params(ax):
ax.tick_params(direction='out', length=2, width=0.5)
ax.spines['bottom'].set_linewidth(0.5)
ax.spines['top'].set_linewidth(0)
ax.spines['left'].set_linewidth(0.5)
ax.spines['right'].set_linewidth(0)
ax.tick_params(labelsize=6)
ax.tick_params(axis='x', which='major', pad=2.0)
ax.tick_params(axis='y', which='major', pad=2.0)
def plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, title='', ko_e18_cols=None):
labels_lst = []
c_i = 5
fig, ax = plt.subplots()
for i, g in enumerate(gene_names):
ax.plot(['FB', 'MB', 'HB', 'SC'], df_training[w_e18_cols].values[gene_idxs[i]], label=g, c=sci_colour[c_i])
labels_lst.append(f'WT {g}')
if ko_e18_cols:
labels_lst.append(f'Eed-cKO {g}')
ax.plot(['FB', 'MB', 'HB', 'SC'], df_training[ko_e18_cols].values[gene_idxs[i]], label=g, c=sci_colour[c_i], linestyle='dashed')
c_i += 1
if c_i == len(sci_colour):
c_i = 0
plt.title(f'{title}')
ax.tick_params(labelsize=6)
#ax.set_ylim(0, 7)
ax.legend(labels_lst, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=6)
set_ax_params(ax)
save_fig(f'line_time_{title}')
plt.show()
def plot_time_line(w_fb_cols, gene_idxs, gene_names, df_training, title='', ko_fb_cols=None):
labels_lst = []
c_i = 5
fig, ax = plt.subplots()
for i, g in enumerate(gene_names):
plt.plot(['E11.5', 'E13.5', 'E15.5', 'E18.5'], df_training[w_fb_cols].values[gene_idxs[i]], label=g, c=sci_colour[c_i])
labels_lst.append(f'WT {g}')
if ko_fb_cols:
plt.plot(['E11.5', 'E13.5', 'E15.5', 'E18.5'], df_training[ko_fb_cols].values[gene_idxs[i]], label=g, c=sci_colour[c_i], linestyle='dashed')
labels_lst.append(f'Eed-cKO {g}')
c_i += 1
if c_i == len(sci_colour):
c_i = 0
plt.title(f'{title}')
ax.tick_params(labelsize=6)
#ax.set_ylim(0, 7)
ax.legend(labels_lst, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=6)
set_ax_params(ax)
save_fig(f'line_tissue_{title}')
plt.show()
def get_gis(genes):
gene_idxs = []
gene_names = []
for i, g in enumerate(df_training[gene_name].values):
if g in genes:
gene_idxs.append(i)
gene_names.append(g)
return gene_idxs, gene_names
e11_cols = [c for c in df_training if 'ko' in c and '11' in c and 'merged' in c]
e13_cols = [c for c in df_training if 'ko' in c and '13' in c and 'merged' in c]
e15_cols = [c for c in df_training if 'ko' in c and '15' in c and 'merged' in c]
e18_cols = [c for c in df_training if 'ko' in c and '18' in c and 'merged' in c]
w_e11_cols = [c for c in df_training if 'wt' in c and '11' in c and 'merged' in c]
w_e13_cols = [c for c in df_training if 'wt' in c and '13' in c and 'merged' in c]
w_e15_cols = [c for c in df_training if 'wt' in c and '15' in c and 'merged' in c]
w_e18_cols = [c for c in df_training if 'wt' in c and '18' in c and 'merged' in c]
ko_fb_cols = [c for c in df_training if 'ko' in c and 'fb' in c and 'merged' in c]
wt_fb_cols = [c for c in df_training if 'wt' in c and 'fb' in c and 'merged' in c]
genes = ['Sox1', 'Sox2', 'Sox3']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'Sox', e18_cols)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'Sox', ko_fb_cols)
genes = ['Eomes', 'Foxg1', 'Tbr1', 'Emx1', 'Lhx6']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'FB', e18_cols)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'FB WT')
plot_tissue_line(e18_cols, gene_idxs, gene_names, df_training, 'FB KO')
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'FB', ko_fb_cols)
genes = ['Otx2', 'Lmx1a', 'En1', 'En2', 'Bhlhe23', 'Sall4']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'MB', e18_cols)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'MB', ko_fb_cols)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'MB WT')
plot_tissue_line(e18_cols, gene_idxs, gene_names, df_training, 'MB KO')
genes = ['Phox2b', 'Krox20', 'Fev', 'Hoxb1', 'Hoxd3']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'HB', e18_cols)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'HB', ko_fb_cols)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'HB WT')
plot_tissue_line(e18_cols, gene_idxs, gene_names, df_training, 'HB KO')
genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 'Hoxa13',
'Hoxb9', 'Hoxb13', 'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'SC', e18_cols)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'SC', ko_fb_cols)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'SC WT')
plot_tissue_line(e18_cols, gene_idxs, gene_names, df_training, 'SC KO')
genes = ['Chek1', 'Chek2', 'Wee1', 'Cdkn1a', 'Cdkn1b', 'Cdkn2a', 'Cdkn1c', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d', 'Rb1']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'Anti', e18_cols)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'Anti', ko_fb_cols)
genes = ['Eed', 'Ezh2', 'Suz12']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'EED', e18_cols)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'EED')
genes = ['Ccna1', 'Ccna2', 'Ccnb1', 'Ccnb2', 'Ccnb3', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'Cdk1', 'Wee2',
'Ccnd1', 'E2f1', 'E2f2', 'E2f3', 'Cdk2', 'Cdk4', 'Cdk6', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'Pro', e18_cols)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'Pro', ko_fb_cols)
import statsmodels.stats.api as sms
def plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, title='', ko_e18_cols=None):
labels_lst = []
c_i = 5
wt_colour = '#5ebce5'
fig, ax = plt.subplots()
wt_values = []
ko_values = []
for i, g in enumerate(gene_names):
labels_lst.append(f'WT {g}')
wt_values.append(df_training[w_e18_cols].values[gene_idxs[i]])
if ko_e18_cols:
labels_lst.append(f'Eed-cKO {g}')
ko_values.append(df_training[ko_e18_cols].values[gene_idxs[i]])
c_i += 1
if c_i == len(sci_colour):
c_i = 0
wt_values = np.array(wt_values)
ko_values = np.array(ko_values)
wt_mean = np.mean(wt_values, axis=0)
ko_mean = np.mean(ko_values, axis=0)
wt_yerr = np.std(wt_values, axis=0)/np.sqrt(len(wt_values[0]))
ko_yerr = np.std(ko_values, axis=0)/np.sqrt(len(ko_values[0]))
(_, caps, _) = ax.errorbar(['FB', 'MB', 'HB', 'SC'], wt_mean, yerr=wt_yerr,
fmt='o', markersize=3, capsize=5, c=wt_colour, alpha=1.0, linewidth=2.0)
for cap in caps:
cap.set_markeredgewidth(1)
(_, caps, _) = ax.errorbar(['FB', 'MB', 'HB', 'SC'], ko_mean, yerr=ko_yerr, fmt='o', markersize=3, capsize=5,
c=ko_colour, alpha=1.0, linewidth=2.0)
for cap in caps:
cap.set_markeredgewidth(1)
ax.plot(['FB', 'MB', 'HB', 'SC'], wt_mean, c=wt_colour, alpha=1.0, linewidth=1.0)
ax.plot(['FB', 'MB', 'HB', 'SC'], ko_mean, c=ko_colour, alpha=1.0, linewidth=1.0)
plt.title(f'{title}')
ax.tick_params(labelsize=6)
ax.set_ylim(0, 8)
set_ax_params(ax)
save_fig(f'line_time_{title}')
plt.show()
plt.show()
def plot_time_line(w_fb_cols, gene_idxs, gene_names, df_training, title='', ko_fb_cols=None):
labels_lst = []
c_i = 5
fig, ax = plt.subplots()
wt_values = []
ko_values = []
wt_colour = '#5ebce5'
for i, g in enumerate(gene_names):
wt_values.append(df_training[w_fb_cols].values[gene_idxs[i]])
labels_lst.append(f'WT {g}')
if ko_fb_cols:
ko_values.append(df_training[ko_fb_cols].values[gene_idxs[i]])
labels_lst.append(f'Eed-cKO {g}')
c_i += 1
if c_i == len(sci_colour):
c_i = 0
wt_values = np.array(wt_values)
ko_values = np.array(ko_values)
wt_mean = np.mean(wt_values, axis=0)
ko_mean = np.mean(ko_values, axis=0)
wt_yerr = np.std(wt_values, axis=0)/np.sqrt(len(wt_values[0]))
ko_yerr = np.std(ko_values, axis=0)/np.sqrt(len(ko_values[0]))
(_, caps, _) = ax.errorbar(['E11.5', 'E13.5', 'E15.5', 'E18.5'], wt_mean, yerr=wt_yerr,
fmt='o', markersize=3, capsize=5, c=wt_colour, alpha=1.0, linewidth=2.0)
for cap in caps:
cap.set_markeredgewidth(1)
(_, caps, _) = ax.errorbar(['E11.5', 'E13.5', 'E15.5', 'E18.5'], ko_mean, yerr=ko_yerr, fmt='o', markersize=3, capsize=5,
c=ko_colour, alpha=1.0, linewidth=2.0)
for cap in caps:
cap.set_markeredgewidth(1)
ax.plot(['E11.5', 'E13.5', 'E15.5', 'E18.5'], wt_mean, c=wt_colour, alpha=1.0, linewidth=1.0)
ax.plot(['E11.5', 'E13.5', 'E15.5', 'E18.5'], ko_mean, c=ko_colour, alpha=1.0, linewidth=1.0)
plt.title(f'{title}')
ax.tick_params(labelsize=6)
ax.set_ylim(0, 8)
ax.legend(labels_lst, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=6)
set_ax_params(ax)
save_fig(f'line_tissue_{title}')
plt.show()
fb_genes = ['Dlx1', 'Dlx5', 'Dlx2', 'Lhx6', 'Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6', 'Dlx1', 'Dlx2', 'Dlx5', 'Nr2e2']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b']
sc_genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 'Hoxa13',
'Hoxb9', 'Hoxb13', 'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']
genes = fb_genes
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'FB', e18_cols)
genes = mb_genes
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'MB', e18_cols)
genes = hb_genes
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'HB', e18_cols)
genes = sc_genes #[ 'Hoxa11', 'Hoxa13', 'Hoxb13', 'Hoxc10', 'Hoxc12', 'Hoxc13']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, 'SC', e18_cols)
genes = ['Ccnb1',
'Cdc25c',
'E2f1',
'Ccna2',
'Ccnd1']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e13_cols, gene_idxs, gene_names, df_training, 'Pro-prolif', e13_cols)
genes = ['Sox1', 'Sox2', 'Sox3']
gene_idxs, gene_names = get_gis(genes)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'Sox', ko_fb_cols)
genes = ['Eed', 'Ezh2', 'Suz12', 'Ezh1']
gene_idxs, gene_names = get_gis(genes)
plot_time_line(wt_fb_cols, gene_idxs, gene_names, df_training, 'Eed', ko_fb_cols)
# ['Ccnb1',
# 'Cdc25c',
# 'E2f1',
# 'Ccna2',
# 'Ccnd1']
genes = ['Ccna1', 'Ccna2', 'Ccnb1', 'Ccnb2', 'Ccnb3', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'Cdk1', 'Wee2',
'Ccnd1', 'E2f1', 'E2f2', 'E2f3', 'Cdk2', 'Cdk4', 'Cdk6', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e13_cols, gene_idxs, gene_names, df_training, 'Pro-prolif-sig', e13_cols)
# Do the anti prolif const aff
genes = [
'Cdkn1a',
'Cdkn2a',
'Cdkn2b']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e13_cols, gene_idxs, gene_names, df_training, 'Anti-prolif', e13_cols)
# Do the anti prolif sig aff
genes = ['Chek1', 'Chek2', 'Wee1', 'Cdkn1a', 'Cdkn1b', 'Cdkn2a', 'Cdkn1c', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d', 'Rb1']
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e13_cols, gene_idxs, gene_names, df_training, 'Anti-prolif-sig', e13_cols)
def plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, title='', ko_e18_cols=None):
labels_lst = []
c_i = 5
wt_colour = '#5ebce5'
fig, ax = plt.subplots()
for i, g in enumerate(gene_names):
ax.plot(['FB', 'MB', 'HB', 'SC'], df_training[w_e13_cols].values[gene_idxs[i]], c=wt_colour, linewidth=2.0, linestyle="dotted")
ax.plot(['FB', 'MB', 'HB', 'SC'], df_training[w_e15_cols].values[gene_idxs[i]], c=wt_colour, linewidth=2.0, linestyle="dashed")
ax.plot(['FB', 'MB', 'HB', 'SC'], df_training[w_e18_cols].values[gene_idxs[i]], c=wt_colour, linewidth=2.0)
labels_lst.append(f'WT {g}')
if ko_e18_cols:
labels_lst.append(f'Eed-cKO {g}')
ax.plot(['FB', 'MB', 'HB', 'SC'], df_training[e13_cols].values[gene_idxs[i]], label=g, c=ko_colour, linewidth=2.0, linestyle="dotted")
ax.plot(['FB', 'MB', 'HB', 'SC'], df_training[e15_cols].values[gene_idxs[i]], label=g, c=ko_colour, linewidth=2.0, linestyle="dashed")
ax.plot(['FB', 'MB', 'HB', 'SC'], df_training[e18_cols].values[gene_idxs[i]], label=g, c=ko_colour, linewidth=2.0)
c_i += 1
if c_i == len(sci_colour):
c_i = 0
plt.title(f'{title}')
ax.tick_params(labelsize=6)
set_ax_params(ax)
save_fig(f'line_time_{title}')
plt.show()
def plot_time_line(w_fb_cols, gene_idxs, gene_names, df_training, title='', ko_fb_cols=None):
labels_lst = []
wt_colour = '#5ebce5'
c_i = 5
fig, ax = plt.subplots()
for i, g in enumerate(gene_names):
plt.plot(['E11.5', 'E13.5', 'E15.5', 'E18.5'], df_training[w_fb_cols].values[gene_idxs[i]], label=g, c=sci_colour[c_i])
labels_lst.append(f'WT {g}')
if ko_fb_cols:
plt.plot(['E11.5', 'E13.5', 'E15.5', 'E18.5'], df_training[ko_fb_cols].values[gene_idxs[i]], label=g, c=sci_colour[c_i], linestyle='dashed')
labels_lst.append(f'Eed-cKO {g}')
c_i += 1
if c_i == len(sci_colour):
c_i = 0
plt.title(f'{title}')
ax.tick_params(labelsize=6)
#ax.set_ylim(0, 7)
ax.legend(labels_lst, loc='center left', bbox_to_anchor=(1, 0.5), fontsize=6)
set_ax_params(ax)
save_fig(f'line_tissue_{title}')
plt.show()
for g in ['Cdkn2a', 'Cdkn2b', 'Cdkn1a', 'Chek1', 'Chek2', 'Wee1', 'Cdkn1a', 'Cdkn1b']:
genes = [g] #, 'Cdkn1a', 'Cdkn1c', 'Cdkn2c']
# 'Chek1', 'Chek2', 'Wee1', 'Cdkn1a', 'Cdkn1b', 'Cdkn2d', 'Rb1'
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, g, e18_cols)
for g in ['Cdk1', 'Cdc25c', 'E2f1', 'Ccna2']:
genes = [g] #, 'Cdkn1a', 'Cdkn1c', 'Cdkn2c']
# 'Chek1', 'Chek2', 'Wee1', 'Cdkn1a', 'Cdkn1b', 'Cdkn2d', 'Rb1'
gene_idxs, gene_names = get_gis(genes)
plot_tissue_line(w_e18_cols, gene_idxs, gene_names, df_training, g, e18_cols)
Since the VAEs are stochastic rather than deterministic we want to create our clusters based on a number of iterations. We also are interested in how much the genes differ in various latent spaces.
from scivae import VAE
hist_metric = 'signal'
labels = df_training[gene_name].values
# -----------------------------------------------------------------------------------
# Set the input data space
# -----------------------------------------------------------------------------------
def get_input_data(df: pd.DataFrame, label_col: str):
scaler = MinMaxScaler(copy=True)
""" Here we define the data for the VAE we choose to log2 the signal """
cols_bin = []
tmp_df = pd.DataFrame()
non_nan_df = df.copy()
non_nan_df = non_nan_df.fillna(0)
for c in df.columns:
if 'H3K27me3' in c and 'signal' in c and 'brain' in c:
v = np.log2(non_nan_df[c].values + 1)
nn_min = np.nanmin(v)
nn_max = np.nanmax(v)
tmp_df[f'{c}_log2'] = (v-nn_min) / (nn_max - nn_min)
cols_bin.append(f'{c}_log2')
elif ('merged-rep' not in c) and ('log2FoldChange' in c or 'wt' in c or 'ko' in c):
cols_bin.append(c)
v = non_nan_df[c].values
nn_min = np.nanmin(v)
nn_max = np.nanmax(v)
tmp_df[c] = (v-nn_min) / (nn_max - nn_min)
vae_values = tmp_df[cols_bin].values
vae_values = np.nan_to_num(vae_values)
tmp_df[label_col] = df[label_col].values
return vae_values, tmp_df, df[label_col].values
# # -----------------------------------------------------------------------------------
# # Run a test to work out the best number of nodes
# # -----------------------------------------------------------------------------------
# vae_input_values, df_input, labels = get_input_data(df_training, gene_name)
# tsts = [1, 2, 3, 4, 6, 8, 16, 24]
# for i in tsts:
# for j in range(0, 5):
# config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 2.0},
# 'encoding': {'layers': [{'num_nodes': 64, 'activation_fn': 'selu'},
# {'num_nodes': 32, 'activation_fn': 'relu'}]},
# 'decoding': {'layers': [{'num_nodes': 32, 'activation_fn': 'relu'},
# {'num_nodes': 64, 'activation_fn': 'selu'}]},
# 'latent': {'num_nodes': i}, 'optimiser': {'params': {}, 'name': 'adam'}}
# vae_mse = VAE(vae_input_values, vae_input_values, labels, config, f'vae_num-nodes_{i}_rep_{j}')
# vae_mse.encode('default', epochs=100, batch_size=50,
# logging_dir=logging_dir, logfile=f'run_num-nodes_{i}_rep_{j}.csv')
# # -----------------------------------------------------------------------------------
# # Plot results
# # -----------------------------------------------------------------------------------
# # Read in each of the csv's and then add a line for each one
# tsts = [1, 2, 3, 4, 6, 8, 16, 24]
# fig, ax = plt.subplots()
# labels = []
# for i, t in enumerate(tsts):
# c = sci_colour[i]
# print(c, i)
# for j in range(0, 5):
# d = pd.read_csv(f'{logging_dir}run_num-nodes_{t}_rep_{j}.csv')
# print(f'{logging_dir}run_num-nodes_{t}_rep_{j}.csv')
# ax.plot(d['epoch'].values, d['loss'].values, c=c, alpha=0.5)
# label=f'{t} node(s)'
# if label not in labels:
# ax.plot(d['epoch'].values, d['val_loss'].values, c=c, alpha=0.8, label=label)
# labels.append(label)
# else:
# ax.plot(d['epoch'].values, d['val_loss'].values, c=c, alpha=0.8)
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
# axes = plt.gca()
# axes.set_ylim([0, 5])
# save_fig(f'node_num')
# plt.show()
# -----------------------------------------------------------------------------------
# Based on the above, set the number of nodes
# 1) Build non-linear VAE
# 2) Build linear VAE
# -----------------------------------------------------------------------------------
num_nodes = 3
vae_input_values, df_input, labels = get_input_data(df_training, gene_name)
config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 1.0,
'mmcd_method': 'k'},
'encoding': {'layers': [{'num_nodes': 64, 'activation_fn': 'selu'},
{'num_nodes': 32, 'activation_fn': 'relu'}
]},
'decoding': {'layers': [
{'num_nodes': 32, 'activation_fn': 'relu'},
{'num_nodes': 64, 'activation_fn': 'selu'}]},
'latent': {'num_nodes': num_nodes}, 'optimiser': {'params': {}, 'name': 'adam'}}
vae_mse = VAE(vae_input_values, vae_input_values, labels, config, f'final')
if not from_saved:
vae_mse.encode('default', epochs=250, batch_size=50,
logging_dir="final_vae_logs", logfile=f'final.csv')
vae_mse.save() # save the VAE
vae_mse.u.dp(["Saved VAE to current directory."])
else:
vae_mse.u.dp(["Trying to load saved VAE..."])
vae_mse.load()
vae_mse.u.dp(["Loaded saved VAE :) "])
df = df_training.copy()
# Encode the values using the VAE
scaler = MinMaxScaler(copy=True)
scaled_vals = scaler.fit_transform(vae_input_values)
vae_data = vae_mse.encode_new_data(scaled_vals)
None -------------------------------------------------------------------------------- Trying to load saved VAE... -------------------------------------------------------------------------------- Model: "encoder" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== default_input (InputLayer) [(None, 97)] 0 __________________________________________________________________________________________________ dense_448 (Dense) (None, 64) 6272 default_input[0][0] __________________________________________________________________________________________________ dense_449 (Dense) (None, 32) 2080 dense_448[0][0] __________________________________________________________________________________________________ z_mean (Dense) (None, 3) 99 dense_449[0][0] __________________________________________________________________________________________________ z_log_sigma (Dense) (None, 3) 99 dense_449[0][0] __________________________________________________________________________________________________ z (Lambda) (None, 3) 0 z_mean[0][0] z_log_sigma[0][0] ================================================================================================== Total params: 8,550 Trainable params: 8,550 Non-trainable params: 0 __________________________________________________________________________________________________ Model: "decoder" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= z_sampling (InputLayer) [(None, 3)] 0 _________________________________________________________________ dense_450 (Dense) (None, 32) 128 _________________________________________________________________ dense_451 (Dense) (None, 64) 2112 _________________________________________________________________ dense_452 (Dense) (None, 97) 6305 ================================================================= Total params: 8,545 Trainable params: 8,545 Non-trainable params: 0 _________________________________________________________________ Model: "final_scivae" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== default_input (InputLayer) [(None, 97)] 0 __________________________________________________________________________________________________ encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0] __________________________________________________________________________________________________ decoder (Functional) (None, 97) 8545 encoder[0][2] __________________________________________________________________________________________________ dense_448 (Dense) (None, 64) 6272 default_input[0][0] __________________________________________________________________________________________________ dense_449 (Dense) (None, 32) 2080 dense_448[0][0] __________________________________________________________________________________________________ z_mean (Dense) (None, 3) 99 dense_449[0][0] __________________________________________________________________________________________________ z_log_sigma (Dense) (None, 3) 99 dense_449[0][0] __________________________________________________________________________________________________ z (Lambda) (None, 3) 0 z_mean[0][0] z_log_sigma[0][0] __________________________________________________________________________________________________ tf_op_layer_Shape_1480 (TensorF [(2,)] 0 z[0][0] __________________________________________________________________________________________________ tf_op_layer_strided_slice_1480 [()] 0 tf_op_layer_Shape_1480[0][0] __________________________________________________________________________________________________ tf_op_layer_Pack_148 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1480[0] __________________________________________________________________________________________________ tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_148[0][0] __________________________________________________________________________________________________ tf_op_layer_Mul_444 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_ __________________________________________________________________________________________________ tf_op_layer_Add_148 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_444[0][0] __________________________________________________________________________________________________ tf_op_layer_Shape_1481 (TensorF [(2,)] 0 tf_op_layer_Add_148[0][0] 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tf_op_layer_Mean_1040 (TensorFl [()] 0 tf_op_layer_Exp_445[0][0] __________________________________________________________________________________________________ tf_op_layer_Mean_1041 (TensorFl [()] 0 tf_op_layer_Exp_446[0][0] __________________________________________________________________________________________________ tf_op_layer_Sub_744 (TensorFlow [(None, 97)] 0 default_input[0][0] decoder[0][0] __________________________________________________________________________________________________ tf_op_layer_AddV2_296 (TensorFl [()] 0 tf_op_layer_Mean_1039[0][0] tf_op_layer_Mean_1040[0][0] __________________________________________________________________________________________________ tf_op_layer_Mul_445 (TensorFlow [()] 0 tf_op_layer_Mean_1041[0][0] __________________________________________________________________________________________________ tf_op_layer_Square_595 (TensorF [(None, 97)] 0 tf_op_layer_Sub_744[0][0] __________________________________________________________________________________________________ tf_op_layer_Sub_743 (TensorFlow [()] 0 tf_op_layer_AddV2_296[0][0] tf_op_layer_Mul_445[0][0] __________________________________________________________________________________________________ tf_op_layer_Sum_148 (TensorFlow [(None,)] 0 tf_op_layer_Square_595[0][0] __________________________________________________________________________________________________ tf_op_layer_Mul_446 (TensorFlow [()] 0 tf_op_layer_Sub_743[0][0] __________________________________________________________________________________________________ tf_op_layer_AddV2_297 (TensorFl [(None,)] 0 tf_op_layer_Sum_148[0][0] tf_op_layer_Mul_446[0][0] __________________________________________________________________________________________________ tf_op_layer_Mean_1042 (TensorFl [()] 0 tf_op_layer_AddV2_297[0][0] __________________________________________________________________________________________________ add_loss_148 (AddLoss) () 0 tf_op_layer_Mean_1042[0][0] ================================================================================================== Total params: 17,095 Trainable params: 17,095 Non-trainable params: 0 __________________________________________________________________________________________________ None -------------------------------------------------------------------------------- Loaded saved VAE :) --------------------------------------------------------------------------------
Here we setup the data and summarise key features, for example calcualting the median values.
We do this so we can easily access these later.
# -----------------------------------------------------------------------------------
# Data setup for Vis
# -----------------------------------------------------------------------------------
hist_metric = 'signal'
input_cols = list(df_input.columns)
def get_median(df, mark, time="", tissue="brain"):
cols = []
for c in df.columns:
if tissue in c and mark in c and hist_metric in c and time in c and 'median' not in c:
cols.append(c)
# get nan median
vals = np.nanmedian(df[cols].values, axis=1)
return np.nan_to_num(vals)
marks = ['H3K27me3', 'H3K4me1', 'H3K4me2', 'H3K4me3', 'H3K27ac', 'H3K9me3', 'H3K36me3', 'H3K9ac']
# Get the median over all time points and all brain tissues
for m in marks:
df[f'{m}'] = get_median(df, m, time="", tissue="brain")
times = ['10.5', '11.5', '12.5', '13.5', '14.5', '15.5', '16.5']
for m in marks:
for e in times:
df[f'median_brain_{e}_signal_{m}'] = get_median(df, m, time=e, tissue="brain")
tissues = ['hindbrain', 'midbrain', 'forebrain']
for m in marks:
for e in times:
for t in tissues:
df[f'median_{t}_{e}_signal_{m}'] = get_median(df, m, time=e, tissue=t)
df['mean_wt'] = np.mean(df[[c for c in df.columns if 'wt' in c]].values, axis=1)
df['mean_ko'] = np.mean(df[[c for c in df.columns if 'ko' in c]].values, axis=1)
df['mean_ko-wt'] = df['mean_ko'].values - df['mean_wt'].values
# Add in the VAE columns
for v in range(0, len(vae_data[0])):
df[f'VAE{v}'] = vae_data[:, v]
# Rename/add some columns just to make figs easier
df['Log2FC FB'] = df['log2FoldChange_fb'].values
df['Log2FC MB'] = df['log2FoldChange_mb'].values
df['Log2FC HB'] = df['log2FoldChange_hb'].values
df['Log2FC SC'] = df['log2FoldChange_sc'].values
df['Log2FC A11'] = df['log2FoldChange_a11'].values
df['Log2FC A13'] = df['log2FoldChange_a13'].values
df['Log2FC A15'] = df['log2FoldChange_a15'].values
df['Log2FC A18'] = df['log2FoldChange_a18'].values
df['Log2FC P11'] = df['log2FoldChange_p11'].values
df['Log2FC P13'] = df['log2FoldChange_p13'].values
df['Log2FC P15'] = df['log2FoldChange_p15'].values
df['Log2FC P18'] = df['log2FoldChange_p18'].values
all_cols = [
'H3K9me3',
'H3K4me1',
'H3K4me2',
'H3K4me3',
'H3K27ac',
'H3K36me3',
'H3K9ac',
'mean_wt',
'mean_ko',
'mean_ko-wt',
'H3K27me3',
'Log2FC FB',
'Log2FC MB',
'Log2FC HB',
'Log2FC SC',
'VAE0',
'VAE1',
'VAE2',
'Log2FC A11',
'Log2FC A13',
'Log2FC A15',
'Log2FC A18',
'Log2FC P11',
'Log2FC P13',
'Log2FC P15',
'Log2FC P18'
]
unobserved_cols = [
'H3K9me3',
'H3K4me1',
'H3K4me2',
'H3K4me3',
'H3K27ac',
'H3K36me3',
'H3K9ac',
'mean_wt',
'mean_ko',
'mean_ko-wt'
]
observed_cols = [
'H3K4me1',
'H3K4me2',
'H3K4me3',
'H3K27ac',
'H3K36me3',
'H3K27me3',
'Log2FC FB',
'Log2FC MB',
'Log2FC HB',
'Log2FC SC',
'VAE0',
'VAE1',
'VAE2'
]
hist_cols = [
'H3K9me3',
'H3K4me1',
'H3K4me2',
'H3K4me3',
'H3K27ac',
'H3K36me3',
'H3K27me3',
'H3K9ac'
]
div_cols = [
'Log2FC FB',
'Log2FC MB',
'Log2FC HB',
'Log2FC SC',
'Log2FC A11',
'Log2FC A13',
'Log2FC A15',
'Log2FC A18',
'Log2FC P11',
'Log2FC P13',
'Log2FC P15',
'Log2FC P18',
]
raw_cols = [
'mean_wt',
'mean_ko'
]
vae_cols = observed_cols
df_unobserved = df[unobserved_cols]
df_observed = df[observed_cols]
observed_dict = {}
for c in observed_cols:
observed_dict[c] = df_observed[c].values
unobserved_dict = {}
for c in unobserved_cols:
unobserved_dict[c] = df_unobserved[c].values
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1113: RuntimeWarning: Mean of empty slice return np.nanmean(a, axis, out=out, keepdims=keepdims)
Here we want to plot how correlated each node is with the mean values of different epigenetic marks & also the means of input features in the dataset.
We also plot the latent space coloured by each of the features (both observed and unobserved).
Lastly, we plot heatmaps of the input, latent and decoding results for key marker genes so that we can work out whether our model is logical. These same genes are also projected onto the latent space where we can see that the different classes of markers (e.g. Hox genes vs brain genes) are easily discernable.
# -----------------------------------------------------------------------------------
# Plot scatter of the features
# -----------------------------------------------------------------------------------
all_vae_data = np.copy(vae_data)
vae_data = all_vae_data[:, 0:3]
vae_vis = VVis(vae_mse, u, None)
div_cmap = 'seismic'
feature_obs_ax = vae_vis.plot_feature_scatters(df, "", columns=hist_cols, vae_data=vae_data,
show_plt=False, output_dir=fig_dir, fig_type="svg",
save_fig=False, title=f'{experiment_name}',
angle_plot=315, cmap=hist_cmap, vmin=0, vmax=10)
feature_obs_ax = vae_vis.plot_feature_scatters(df, "", columns=div_cols, vae_data=vae_data,
show_plt=False, output_dir=fig_dir, fig_type="svg",
save_fig=False, title=f'{experiment_name}',
angle_plot=315, cmap=div_cmap, vmin=-10, vmax=10)
feature_obs_ax = vae_vis.plot_feature_scatters(df, "", columns=raw_cols, vae_data=vae_data,
show_plt=False, output_dir=fig_dir, fig_type="svg",
save_fig=False, title=f'{experiment_name}',
angle_plot=315, cmap=rna_cmap, vmin=0, vmax=10)
# -----------------------------------------------------------------------------------
# Plot histograms
# -----------------------------------------------------------------------------------
vae_vis.plot_node_hists(show_plt=True, save_fig=False)
vae_vis.plot_input_distribution(df_observed, show_plt=True, save_fig=True, output_dir=fig_dir)
vae_vis.plot_input_distribution(df_unobserved, show_plt=True, output_dir=fig_dir, save_fig=True)
# -----------------------------------------------------------------------------------
# Plot correlations
# -----------------------------------------------------------------------------------
vae_vis.plot_node_correlation(show_plt=True, save_fig=False)
vae_vis.plot_node_feature_correlation(df_observed, '', columns=observed_cols, vae_data=vae_data,
show_plt=True, output_dir=fig_dir, save_fig=True,
title=f'Node vs observed features {experiment_name}', cmap=div_cmap)
vae_vis.plot_feature_correlation(df_observed, '', columns=observed_cols, show_plt=True, cmap=div_cmap,
title=f'Feature vs observed features {experiment_name}',
output_dir=fig_dir, save_fig=True)
vae_vis.plot_node_feature_correlation(df_unobserved, '', columns=unobserved_cols, vae_data=vae_data,
cmap=div_cmap,
show_plt=True, output_dir=fig_dir, save_fig=True,
title=f'Node vs unobserved features {experiment_name}')
vae_vis.plot_feature_correlation(df_unobserved, '', columns=unobserved_cols, show_plt=True, cmap=div_cmap,
output_dir=fig_dir, save_fig=True, title=f'feature un-observed features {experiment_name}')
vae_vis.plot_feature_correlation(df, '', columns=unobserved_cols + observed_cols, show_plt=True, cmap=div_cmap,
output_dir=fig_dir, save_fig=True, title=f'features all {experiment_name}')
vae_vis.plot_feature_correlation(df, '', columns=vae_cols, show_plt=True, cmap=div_cmap,
output_dir=fig_dir, save_fig=True, title=f'features VAE {experiment_name}')
# -----------------------------------------------------------------------------------
# Plot heatmaps of marker genes
# -----------------------------------------------------------------------------------
fb_genes = ['Foxg1']
mb_genes = ['En2']
hb_genes = ['Sox3']
sc_genes = ['Hoxc9']
input_cols = [c for c in df_input.columns if c != gene_name]
merged_cols = [c for c in df.columns if "merged" in c]
# Plot the input space
plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes,
merged_cols, vae_data, vae_mse,
gene_name, mark='H3K27me3', method='input', cmap=hist_cmap)
# Plot the decoding space
plot_gene_heatmap(df_input, fb_genes, mb_genes, hb_genes, sc_genes,
input_cols, vae_data, vae_mse,
gene_name, mark='H3K27me3', method='decoding', cmap=div_cmap)
# Plot the latent space
plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes,
['VAE0', 'VAE1', 'VAE2'], vae_data, vae_mse,
gene_name, mark='H3K27me3', method='latent', cmap=hist_cmap)
# Also do the main markers
fb_genes = ['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Phox2b', 'Krox20', 'Fev', 'Hoxb1', 'Hoxd3']
sc_genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11','Hoxa7', 'Hoxa9', 'Hoxa10',
'Hoxb9', 'Hoxb13', 'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/scivae/vis.py:351: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = plt.figure()
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1 1 1.0 0.0 1 2 -0.17728057341748157 7.524072692721458e-91 1 3 0.024112702618906502 0.006374833795794004 2 1 -0.17728057341748157 7.524072692721458e-91 2 2 1.0 0.0 2 3 0.015588291538124632 0.07784281684845336 3 1 0.024112702618906502 0.006374833795794004 3 2 0.015588291538124632 0.07784281684845336 3 3 1.0 0.0
<Figure size 216x216 with 0 Axes>
1 H3K4me1 -0.0446884001956768 4.251104865461492e-07 1 H3K4me2 0.08834956848013696 1.335708453202751e-23 1 H3K4me3 0.30597343061378124 1.8027240014084392e-275 1 H3K27ac 0.3514588028990406 0.0 1 H3K36me3 0.20757601112819188 1.4444036902704797e-124 1 H3K27me3 -0.10520428908479008 7.957836150465658e-33 1 Log2FC FB 0.003582814134617256 0.6852839419752368 1 Log2FC MB -0.17513450220728735 1.1229971545113458e-88 1 Log2FC HB -0.39821806066961 0.0 1 Log2FC SC 0.04095923910797624 3.570096858074452e-06 1 VAE0 1.0 0.0 1 VAE1 -0.17727771491123703 7.574731590825203e-91 1 VAE2 0.024102462909318584 0.006397261512370033 2 H3K4me1 -0.08406049569917753 1.6476006172029343e-21 2 H3K4me2 -0.2636875996916555 1.5735477679806943e-202 2 H3K4me3 0.07296409905287946 1.4064868617096594e-16 2 H3K27ac 0.05627016617662167 1.8912472752821038e-10 2 H3K36me3 0.18686624881923297 6.647406888366817e-101 2 H3K27me3 -0.8608600491616872 0.0 2 Log2FC FB -0.30263401049053534 3.046326192389002e-269 2 Log2FC MB -0.2132191075829392 1.757463064410544e-131 2 Log2FC HB -0.07716379044745164 2.3055509300984496e-18 2 Log2FC SC -0.2449698965314661 3.2652391791142007e-174 2 VAE0 -0.17727771491123703 7.574731590825203e-91 2 VAE1 1.0 0.0 2 VAE2 0.015624110732809882 0.077162199979443 3 H3K4me1 -0.19398340219121096 9.81715077576278e-109 3 H3K4me2 -0.18498827221190545 6.86666738215701e-99 3 H3K4me3 -0.5298862221619517 0.0 3 H3K27ac -0.4482816481694506 0.0 3 H3K36me3 -0.5117903743601302 0.0 3 H3K27me3 0.2279197055845889 1.8859458409143461e-150 3 Log2FC FB 0.5758155364107505 0.0 3 Log2FC MB 0.46938929025701914 0.0 3 Log2FC HB 0.2883184296728831 1.8180376139162956e-243 3 Log2FC SC 0.4631936937538506 0.0 3 VAE0 0.024102462909318584 0.006397261512370033 3 VAE1 0.015624110732809882 0.077162199979443 3 VAE2 1.0 0.0
<Figure size 144x144 with 0 Axes>
H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K27me3 0.03880145459733418 1.1304167715365197e-05 H3K4me1 Log2FC FB -0.14880590787152698 2.8859404622978633e-64 H3K4me1 Log2FC MB -0.10387373575786396 4.872008964191265e-32 H3K4me1 Log2FC HB -0.05777615219411477 6.12378414825415e-11 H3K4me1 Log2FC SC -0.07950151137451965 2.1197279496874784e-19 H3K4me1 VAE0 -0.0446884001956768 4.251104865461492e-07 H3K4me1 VAE1 -0.08406049569917753 1.6476006172029343e-21 H3K4me1 VAE2 -0.19398340219121096 9.81715077576278e-109 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K27me3 0.1836859102996935 1.6625204625607636e-97 H3K4me2 Log2FC FB -0.12969526458358954 3.9896551047753745e-49 H3K4me2 Log2FC MB -0.09642120978180409 8.108800676805556e-28 H3K4me2 Log2FC HB -0.10550208768149451 5.2879602412044856e-33 H3K4me2 Log2FC SC -0.08178812465927765 1.9179743858493656e-20 H3K4me2 VAE0 0.08834956848013696 1.335708453202751e-23 H3K4me2 VAE1 -0.2636875996916555 1.5735477679806943e-202 H3K4me2 VAE2 -0.18498827221190547 6.866667382156619e-99 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K27me3 -0.28958561139696254 1.0906807671975684e-245 H3K4me3 Log2FC FB -0.4470506488127457 0.0 H3K4me3 Log2FC MB -0.4473702882601094 0.0 H3K4me3 Log2FC HB -0.41979679249095553 0.0 H3K4me3 Log2FC SC -0.3297606127199917 0.0 H3K4me3 VAE0 0.30597343061378124 1.8027240014084392e-275 H3K4me3 VAE1 0.07296409905287944 1.4064868617096597e-16 H3K4me3 VAE2 -0.5298862221619517 0.0 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K27me3 -0.2526689336918533 1.4216280599037781e-185 H3K27ac Log2FC FB -0.3815669720025924 0.0 H3K27ac Log2FC MB -0.42208837018425827 0.0 H3K27ac Log2FC HB -0.408298389874337 0.0 H3K27ac Log2FC SC -0.2977765740809075 2.491982785246384e-260 H3K27ac VAE0 0.3514588028990406 0.0 H3K27ac VAE1 0.05627016617662167 1.8912472752821038e-10 H3K27ac VAE2 -0.4482816481694506 0.0 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K27me3 -0.35415360112947697 0.0 H3K36me3 Log2FC FB -0.4744138412763159 0.0 H3K36me3 Log2FC MB -0.430059873023584 0.0 H3K36me3 Log2FC HB -0.3634108408611482 0.0 H3K36me3 Log2FC SC -0.33209079328572233 0.0 H3K36me3 VAE0 0.20757601112819188 1.4444036902704797e-124 H3K36me3 VAE1 0.18686624881923297 6.647406888366817e-101 H3K36me3 VAE2 -0.5117903743601302 0.0 H3K27me3 H3K4me1 0.03880145459733418 1.1304167715365197e-05 H3K27me3 H3K4me2 0.18368591029969353 1.662520462560716e-97 H3K27me3 H3K4me3 -0.2895856113969625 1.0906807671978166e-245 H3K27me3 H3K27ac -0.2526689336918533 1.4216280599037781e-185 H3K27me3 H3K36me3 -0.35415360112947697 0.0 H3K27me3 H3K27me3 1.0 0.0 H3K27me3 Log2FC FB 0.37643342244583206 0.0 H3K27me3 Log2FC MB 0.3225460106810916 1.314030964712288e-307 H3K27me3 Log2FC HB 0.2347875537574898 8.590917791342444e-160 H3K27me3 Log2FC SC 0.2956826706138225 1.5329233866307224e-256 H3K27me3 VAE0 -0.10520428908479008 7.957836150465658e-33 H3K27me3 VAE1 -0.8608600491616873 0.0 H3K27me3 VAE2 0.2279197055845889 1.8859458409143461e-150 Log2FC FB H3K4me1 -0.14880590787152698 2.8859404622978633e-64 Log2FC FB H3K4me2 -0.12969526458358954 3.9896551047753745e-49 Log2FC FB H3K4me3 -0.4470506488127457 0.0 Log2FC FB H3K27ac -0.3815669720025924 0.0 Log2FC FB H3K36me3 -0.4744138412763159 0.0 Log2FC FB H3K27me3 0.37643342244583206 0.0 Log2FC FB Log2FC FB 1.0 0.0 Log2FC FB Log2FC MB 0.7040491449224625 0.0 Log2FC FB Log2FC HB 0.5367783316680511 0.0 Log2FC FB Log2FC SC 0.5730464144717174 0.0 Log2FC FB VAE0 0.0035828141346172554 0.6852839419752368 Log2FC FB VAE1 -0.3026340104905353 3.046326192390388e-269 Log2FC FB VAE2 0.5758155364107505 0.0 Log2FC MB H3K4me1 -0.10387373575786396 4.872008964191265e-32 Log2FC MB H3K4me2 -0.09642120978180409 8.108800676805556e-28 Log2FC MB H3K4me3 -0.44737028826010944 0.0 Log2FC MB H3K27ac -0.4220883701842583 0.0 Log2FC MB H3K36me3 -0.43005987302358406 0.0 Log2FC MB H3K27me3 0.32254601068109157 1.314030964712587e-307 Log2FC MB Log2FC FB 0.7040491449224625 0.0 Log2FC MB Log2FC MB 0.9999999999999999 0.0 Log2FC MB Log2FC HB 0.7578212671301587 0.0 Log2FC MB Log2FC SC 0.5751592030701783 0.0 Log2FC MB VAE0 -0.17513450220728732 1.1229971545114095e-88 Log2FC MB VAE1 -0.21321910758293916 1.757463064410544e-131 Log2FC MB VAE2 0.46938929025701914 0.0 Log2FC HB H3K4me1 -0.05777615219411477 6.12378414825415e-11 Log2FC HB H3K4me2 -0.10550208768149451 5.2879602412044856e-33 Log2FC HB H3K4me3 -0.4197967924909555 0.0 Log2FC HB H3K27ac -0.408298389874337 0.0 Log2FC HB H3K36me3 -0.36341084086114817 0.0 Log2FC HB H3K27me3 0.2347875537574898 8.590917791342444e-160 Log2FC HB Log2FC FB 0.5367783316680511 0.0 Log2FC HB Log2FC MB 0.7578212671301588 0.0 Log2FC HB Log2FC HB 1.0 0.0 Log2FC HB Log2FC SC 0.47172347343211496 0.0 Log2FC HB VAE0 -0.3982180606696101 0.0 Log2FC HB VAE1 -0.07716379044745166 2.3055509300983664e-18 Log2FC HB VAE2 0.2883184296728832 1.8180376139160885e-243 Log2FC SC H3K4me1 -0.07950151137451963 2.1197279496875092e-19 Log2FC SC H3K4me2 -0.08178812465927764 1.9179743858494207e-20 Log2FC SC H3K4me3 -0.3297606127199917 0.0 Log2FC SC H3K27ac -0.2977765740809076 2.4919827852461e-260 Log2FC SC H3K36me3 -0.33209079328572233 0.0 Log2FC SC H3K27me3 0.2956826706138225 1.5329233866307224e-256 Log2FC SC Log2FC FB 0.5730464144717174 0.0 Log2FC SC Log2FC MB 0.5751592030701783 0.0 Log2FC SC Log2FC HB 0.47172347343211496 0.0 Log2FC SC Log2FC SC 0.9999999999999999 0.0 Log2FC SC VAE0 0.04095923910797624 3.570096858074452e-06 Log2FC SC VAE1 -0.24496989653146614 3.2652391791136445e-174 Log2FC SC VAE2 0.4631936937538506 0.0 VAE0 H3K4me1 -0.0446884001956768 4.251104865461492e-07 VAE0 H3K4me2 0.08834956848013696 1.335708453202751e-23 VAE0 H3K4me3 0.30597343061378124 1.8027240014084392e-275 VAE0 H3K27ac 0.3514588028990406 0.0 VAE0 H3K36me3 0.20757601112819188 1.4444036902704797e-124 VAE0 H3K27me3 -0.10520428908479008 7.957836150465658e-33 VAE0 Log2FC FB 0.003582814134617256 0.6852839419752368 VAE0 Log2FC MB -0.17513450220728735 1.1229971545113458e-88 VAE0 Log2FC HB -0.39821806066961 0.0 VAE0 Log2FC SC 0.04095923910797624 3.570096858074452e-06 VAE0 VAE0 1.0 0.0 VAE0 VAE1 -0.17727771491123703 7.574731590825203e-91 VAE0 VAE2 0.024102462909318584 0.006397261512370033 VAE1 H3K4me1 -0.08406049569917753 1.6476006172029343e-21 VAE1 H3K4me2 -0.2636875996916555 1.5735477679806943e-202 VAE1 H3K4me3 0.07296409905287946 1.4064868617096594e-16 VAE1 H3K27ac 0.05627016617662167 1.8912472752821038e-10 VAE1 H3K36me3 0.18686624881923297 6.647406888366817e-101 VAE1 H3K27me3 -0.8608600491616872 0.0 VAE1 Log2FC FB -0.30263401049053534 3.046326192389002e-269 VAE1 Log2FC MB -0.2132191075829392 1.757463064410544e-131 VAE1 Log2FC HB -0.07716379044745164 2.3055509300984496e-18 VAE1 Log2FC SC -0.2449698965314661 3.2652391791142007e-174 VAE1 VAE0 -0.17727771491123703 7.574731590825203e-91 VAE1 VAE1 1.0 0.0 VAE1 VAE2 0.015624110732809882 0.077162199979443 VAE2 H3K4me1 -0.19398340219121096 9.81715077576278e-109 VAE2 H3K4me2 -0.18498827221190545 6.86666738215701e-99 VAE2 H3K4me3 -0.5298862221619517 0.0 VAE2 H3K27ac -0.4482816481694506 0.0 VAE2 H3K36me3 -0.5117903743601302 0.0 VAE2 H3K27me3 0.2279197055845889 1.8859458409143461e-150 VAE2 Log2FC FB 0.5758155364107505 0.0 VAE2 Log2FC MB 0.46938929025701914 0.0 VAE2 Log2FC HB 0.2883184296728831 1.8180376139162956e-243 VAE2 Log2FC SC 0.4631936937538506 0.0 VAE2 VAE0 0.024102462909318584 0.006397261512370033 VAE2 VAE1 0.015624110732809882 0.077162199979443 VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
1 H3K9me3 0.09476451187011106 6.375261288317115e-27 1 H3K4me1 -0.0446884001956768 4.251104865461492e-07 1 H3K4me2 0.08834956848013696 1.335708453202751e-23 1 H3K4me3 0.30597343061378124 1.8027240014084392e-275 1 H3K27ac 0.3514588028990406 0.0 1 H3K36me3 0.20757601112819188 1.4444036902704797e-124 1 H3K9ac 0.3679083251474569 0.0 1 mean_wt 0.6150030977542656 0.0 1 mean_ko 0.6344396714685748 0.0 1 mean_ko-wt -0.07009363106114355 2.0505434236585485e-15 2 H3K9me3 -0.07756915894577787 1.5319117975903234e-18 2 H3K4me1 -0.08406049569917753 1.6476006172029343e-21 2 H3K4me2 -0.2636875996916555 1.5735477679806943e-202 2 H3K4me3 0.07296409905287946 1.4064868617096594e-16 2 H3K27ac 0.05627016617662167 1.8912472752821038e-10 2 H3K36me3 0.18686624881923297 6.647406888366817e-101 2 H3K9ac 0.0037976716291406483 0.6675108445696198 2 mean_wt -0.11951742691063598 6.232716507282639e-42 2 mean_ko -0.1719717780022099 1.59630783090709e-85 2 mean_ko-wt -0.3207239010421294 5.667786389040793e-304 3 H3K9me3 0.08363311818464449 2.6275582494385576e-21 3 H3K4me1 -0.19398340219121096 9.81715077576278e-109 3 H3K4me2 -0.18498827221190545 6.86666738215701e-99 3 H3K4me3 -0.5298862221619517 0.0 3 H3K27ac -0.4482816481694506 0.0 3 H3K36me3 -0.5117903743601302 0.0 3 H3K9ac -0.42709438615274053 0.0 3 mean_wt -0.7268051753065692 0.0 3 mean_ko -0.7019731621801955 0.0 3 mean_ko-wt 0.5486240945696327 0.0
<Figure size 144x144 with 0 Axes>
H3K9me3 H3K9me3 1.0 0.0 H3K9me3 H3K4me1 -0.14322560612182772 1.2682004430470273e-59 H3K9me3 H3K4me2 -0.012727232249096996 0.14996053879600038 H3K9me3 H3K4me3 -0.031174294277473532 0.0004201886170928389 H3K9me3 H3K27ac -0.00358288782217438 0.6852778158564187 H3K9me3 H3K36me3 -0.08596619953337728 1.9975762016514672e-22 H3K9me3 H3K9ac 0.0006515620935032757 0.9412490950421215 H3K9me3 mean_wt -0.005812182301496531 0.5108995655637896 H3K9me3 mean_ko 0.0012293357691734912 0.8894077672281322 H3K9me3 mean_ko-wt 0.07171376981069018 4.577998673738538e-16 H3K4me1 H3K9me3 -0.14322560612182772 1.2682004430470273e-59 H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K9ac 0.2614374133944155 5.329955864994627e-199 H3K4me1 mean_wt 0.11747411642321601 1.4678426946564922e-40 H3K4me1 mean_ko 0.11010738724216297 8.198213479177807e-36 H3K4me1 mean_ko-wt -0.12351444023102397 1.0993490972728222e-44 H3K4me2 H3K9me3 -0.012727232249096996 0.14996053879600038 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K9ac 0.468787932774562 0.0 H3K4me2 mean_wt 0.18397384400291664 8.235251256534976e-98 H3K4me2 mean_ko 0.17946752733713214 4.2877666356691384e-93 H3K4me2 mean_ko-wt -0.09888737471837038 3.5238851765166497e-29 H3K4me3 H3K9me3 -0.031174294277473535 0.0004201886170928379 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K9ac 0.8372246669947291 0.0 H3K4me3 mean_wt 0.6172021315146129 0.0 H3K4me3 mean_ko 0.588174302010897 0.0 H3K4me3 mean_ko-wt -0.48552252459057177 0.0 H3K27ac H3K9me3 -0.00358288782217438 0.6852778158564187 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K9ac 0.7602537276873533 0.0 H3K27ac mean_wt 0.5827182905478256 0.0 H3K27ac mean_ko 0.5590745553867865 0.0 H3K27ac mean_ko-wt -0.42205738337847143 0.0 H3K36me3 H3K9me3 -0.08596619953337728 1.9975762016514672e-22 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K9ac 0.46303044184726333 0.0 H3K36me3 mean_wt 0.534749603678121 0.0 H3K36me3 mean_ko 0.5020241405110045 0.0 H3K36me3 mean_ko-wt -0.4851161257262746 0.0 H3K9ac H3K9me3 0.0006515620935032758 0.9412490950421215 H3K9ac H3K4me1 0.2614374133944155 5.329955864994627e-199 H3K9ac H3K4me2 0.46878793277456193 0.0 H3K9ac H3K4me3 0.8372246669947291 0.0 H3K9ac H3K27ac 0.7602537276873533 0.0 H3K9ac H3K36me3 0.46303044184726333 0.0 H3K9ac H3K9ac 1.0 0.0 H3K9ac mean_wt 0.5805856030139771 0.0 H3K9ac mean_ko 0.5592674641511871 0.0 H3K9ac mean_ko-wt -0.39977752635279323 0.0 mean_wt H3K9me3 -0.005812182301496532 0.5108995655637896 mean_wt H3K4me1 0.117474116423216 1.4678426946564922e-40 mean_wt H3K4me2 0.1839738440029166 8.235251256534976e-98 mean_wt H3K4me3 0.617202131514613 0.0 mean_wt H3K27ac 0.5827182905478255 0.0 mean_wt H3K36me3 0.534749603678121 0.0 mean_wt H3K9ac 0.5805856030139771 0.0 mean_wt mean_wt 1.0 0.0 mean_wt mean_ko 0.9918038831526816 0.0 mean_wt mean_ko-wt -0.4845025698158839 0.0 mean_ko H3K9me3 0.0012293357691734912 0.8894077672281322 mean_ko H3K4me1 0.11010738724216297 8.198213479177807e-36 mean_ko H3K4me2 0.17946752733713214 4.2877666356691384e-93 mean_ko H3K4me3 0.5881743020108969 0.0 mean_ko H3K27ac 0.5590745553867866 0.0 mean_ko H3K36me3 0.5020241405110045 0.0 mean_ko H3K9ac 0.5592674641511871 0.0 mean_ko mean_wt 0.9918038831526816 0.0 mean_ko mean_ko 1.0 0.0 mean_ko mean_ko-wt -0.3900643761493835 0.0 mean_ko-wt H3K9me3 0.07171376981069016 4.577998673738638e-16 mean_ko-wt H3K4me1 -0.12351444023102398 1.0993490972728222e-44 mean_ko-wt H3K4me2 -0.09888737471837038 3.5238851765166497e-29 mean_ko-wt H3K4me3 -0.48552252459057177 0.0 mean_ko-wt H3K27ac -0.42205738337847143 0.0 mean_ko-wt H3K36me3 -0.48511612572627455 0.0 mean_ko-wt H3K9ac -0.39977752635279323 0.0 mean_ko-wt mean_wt -0.4845025698158839 0.0 mean_ko-wt mean_ko -0.3900643761493835 0.0 mean_ko-wt mean_ko-wt 1.0 0.0
<Figure size 216x216 with 0 Axes>
H3K9me3 H3K9me3 1.0 0.0 H3K9me3 H3K4me1 -0.14322560612182772 1.2682004430470273e-59 H3K9me3 H3K4me2 -0.012727232249096996 0.14996053879600038 H3K9me3 H3K4me3 -0.031174294277473532 0.0004201886170928389 H3K9me3 H3K27ac -0.00358288782217438 0.6852778158564187 H3K9me3 H3K36me3 -0.08596619953337728 1.9975762016514672e-22 H3K9me3 H3K9ac 0.0006515620935032757 0.9412490950421215 H3K9me3 mean_wt -0.005812182301496531 0.5108995655637896 H3K9me3 mean_ko 0.0012293357691734912 0.8894077672281322 H3K9me3 mean_ko-wt 0.07171376981069018 4.577998673738538e-16 H3K9me3 H3K4me1 -0.14322560612182772 1.2682004430470273e-59 H3K9me3 H3K4me2 -0.012727232249096996 0.14996053879600038 H3K9me3 H3K4me3 -0.031174294277473532 0.0004201886170928389 H3K9me3 H3K27ac -0.00358288782217438 0.6852778158564187 H3K9me3 H3K36me3 -0.08596619953337728 1.9975762016514672e-22 H3K9me3 H3K27me3 0.06719198878685362 2.764988245960282e-14 H3K9me3 Log2FC FB 0.09824918653348803 7.994118616749208e-29 H3K9me3 Log2FC MB 0.019587856642572236 0.02670181757210075 H3K9me3 Log2FC HB -0.008660581682350359 0.32726345641777266 H3K9me3 Log2FC SC 0.025615504235275782 0.0037565089010411284 H3K9me3 VAE0 0.09476451187011106 6.375261288317115e-27 H3K9me3 VAE1 -0.07756915894577787 1.5319117975903234e-18 H3K9me3 VAE2 0.08363311818464449 2.6275582494385576e-21 H3K4me1 H3K9me3 -0.14322560612182772 1.2682004430470273e-59 H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K9ac 0.2614374133944155 5.329955864994627e-199 H3K4me1 mean_wt 0.11747411642321601 1.4678426946564922e-40 H3K4me1 mean_ko 0.11010738724216297 8.198213479177807e-36 H3K4me1 mean_ko-wt -0.12351444023102397 1.0993490972728222e-44 H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K27me3 0.03880145459733418 1.1304167715365197e-05 H3K4me1 Log2FC FB -0.14880590787152698 2.8859404622978633e-64 H3K4me1 Log2FC MB -0.10387373575786396 4.872008964191265e-32 H3K4me1 Log2FC HB -0.05777615219411477 6.12378414825415e-11 H3K4me1 Log2FC SC -0.07950151137451965 2.1197279496874784e-19 H3K4me1 VAE0 -0.0446884001956768 4.251104865461492e-07 H3K4me1 VAE1 -0.08406049569917753 1.6476006172029343e-21 H3K4me1 VAE2 -0.19398340219121096 9.81715077576278e-109 H3K4me2 H3K9me3 -0.012727232249096996 0.14996053879600038 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K9ac 0.468787932774562 0.0 H3K4me2 mean_wt 0.18397384400291664 8.235251256534976e-98 H3K4me2 mean_ko 0.17946752733713214 4.2877666356691384e-93 H3K4me2 mean_ko-wt -0.09888737471837038 3.5238851765166497e-29 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K27me3 0.1836859102996935 1.6625204625607636e-97 H3K4me2 Log2FC FB -0.12969526458358954 3.9896551047753745e-49 H3K4me2 Log2FC MB -0.09642120978180409 8.108800676805556e-28 H3K4me2 Log2FC HB -0.10550208768149451 5.2879602412044856e-33 H3K4me2 Log2FC SC -0.08178812465927765 1.9179743858493656e-20 H3K4me2 VAE0 0.08834956848013696 1.335708453202751e-23 H3K4me2 VAE1 -0.2636875996916555 1.5735477679806943e-202 H3K4me2 VAE2 -0.18498827221190547 6.866667382156619e-99 H3K4me3 H3K9me3 -0.031174294277473535 0.0004201886170928379 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K9ac 0.8372246669947291 0.0 H3K4me3 mean_wt 0.6172021315146129 0.0 H3K4me3 mean_ko 0.588174302010897 0.0 H3K4me3 mean_ko-wt -0.48552252459057177 0.0 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K27me3 -0.28958561139696254 1.0906807671975684e-245 H3K4me3 Log2FC FB -0.4470506488127457 0.0 H3K4me3 Log2FC MB -0.4473702882601094 0.0 H3K4me3 Log2FC HB -0.41979679249095553 0.0 H3K4me3 Log2FC SC -0.3297606127199917 0.0 H3K4me3 VAE0 0.30597343061378124 1.8027240014084392e-275 H3K4me3 VAE1 0.07296409905287944 1.4064868617096597e-16 H3K4me3 VAE2 -0.5298862221619517 0.0 H3K27ac H3K9me3 -0.00358288782217438 0.6852778158564187 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K9ac 0.7602537276873533 0.0 H3K27ac mean_wt 0.5827182905478256 0.0 H3K27ac mean_ko 0.5590745553867865 0.0 H3K27ac mean_ko-wt -0.42205738337847143 0.0 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K27me3 -0.2526689336918533 1.4216280599037781e-185 H3K27ac Log2FC FB -0.3815669720025924 0.0 H3K27ac Log2FC MB -0.42208837018425827 0.0 H3K27ac Log2FC HB -0.408298389874337 0.0 H3K27ac Log2FC SC -0.2977765740809075 2.491982785246384e-260 H3K27ac VAE0 0.3514588028990406 0.0 H3K27ac VAE1 0.05627016617662167 1.8912472752821038e-10 H3K27ac VAE2 -0.4482816481694506 0.0 H3K36me3 H3K9me3 -0.08596619953337728 1.9975762016514672e-22 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K9ac 0.46303044184726333 0.0 H3K36me3 mean_wt 0.534749603678121 0.0 H3K36me3 mean_ko 0.5020241405110045 0.0 H3K36me3 mean_ko-wt -0.4851161257262746 0.0 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K27me3 -0.35415360112947697 0.0 H3K36me3 Log2FC FB -0.4744138412763159 0.0 H3K36me3 Log2FC MB -0.430059873023584 0.0 H3K36me3 Log2FC HB -0.3634108408611482 0.0 H3K36me3 Log2FC SC -0.33209079328572233 0.0 H3K36me3 VAE0 0.20757601112819188 1.4444036902704797e-124 H3K36me3 VAE1 0.18686624881923297 6.647406888366817e-101 H3K36me3 VAE2 -0.5117903743601302 0.0 H3K9ac H3K9me3 0.0006515620935032758 0.9412490950421215 H3K9ac H3K4me1 0.2614374133944155 5.329955864994627e-199 H3K9ac H3K4me2 0.46878793277456193 0.0 H3K9ac H3K4me3 0.8372246669947291 0.0 H3K9ac H3K27ac 0.7602537276873533 0.0 H3K9ac H3K36me3 0.46303044184726333 0.0 H3K9ac H3K9ac 1.0 0.0 H3K9ac mean_wt 0.5805856030139771 0.0 H3K9ac mean_ko 0.5592674641511871 0.0 H3K9ac mean_ko-wt -0.39977752635279323 0.0 H3K9ac H3K4me1 0.2614374133944155 5.329955864994627e-199 H3K9ac H3K4me2 0.46878793277456193 0.0 H3K9ac H3K4me3 0.8372246669947291 0.0 H3K9ac H3K27ac 0.7602537276873533 0.0 H3K9ac H3K36me3 0.46303044184726333 0.0 H3K9ac H3K27me3 -0.2049746737349427 1.9027988176860502e-121 H3K9ac Log2FC FB -0.3676902675182298 0.0 H3K9ac Log2FC MB -0.39013572214451403 0.0 H3K9ac Log2FC HB -0.3936885591832504 0.0 H3K9ac Log2FC SC -0.26251104559696414 1.1139140119303159e-200 H3K9ac VAE0 0.3679083251474569 0.0 H3K9ac VAE1 0.0037976716291406483 0.6675108445696198 H3K9ac VAE2 -0.42709438615274053 0.0 mean_wt H3K9me3 -0.005812182301496532 0.5108995655637896 mean_wt H3K4me1 0.117474116423216 1.4678426946564922e-40 mean_wt H3K4me2 0.1839738440029166 8.235251256534976e-98 mean_wt H3K4me3 0.617202131514613 0.0 mean_wt H3K27ac 0.5827182905478255 0.0 mean_wt H3K36me3 0.534749603678121 0.0 mean_wt H3K9ac 0.5805856030139771 0.0 mean_wt mean_wt 1.0 0.0 mean_wt mean_ko 0.9918038831526816 0.0 mean_wt mean_ko-wt -0.4845025698158839 0.0 mean_wt H3K4me1 0.117474116423216 1.4678426946564922e-40 mean_wt H3K4me2 0.1839738440029166 8.235251256534976e-98 mean_wt H3K4me3 0.617202131514613 0.0 mean_wt H3K27ac 0.5827182905478255 0.0 mean_wt H3K36me3 0.534749603678121 0.0 mean_wt H3K27me3 -0.25633608266022023 3.991995123962663e-191 mean_wt Log2FC FB -0.4478380069404565 0.0 mean_wt Log2FC MB -0.4838366012597243 0.0 mean_wt Log2FC HB -0.506836755731281 0.0 mean_wt Log2FC SC -0.32938626839064894 0.0 mean_wt VAE0 0.6150030977542656 0.0 mean_wt VAE1 -0.11951742691063598 6.232716507282639e-42 mean_wt VAE2 -0.7268051753065692 0.0 mean_ko H3K9me3 0.0012293357691734912 0.8894077672281322 mean_ko H3K4me1 0.11010738724216297 8.198213479177807e-36 mean_ko H3K4me2 0.17946752733713214 4.2877666356691384e-93 mean_ko H3K4me3 0.5881743020108969 0.0 mean_ko H3K27ac 0.5590745553867866 0.0 mean_ko H3K36me3 0.5020241405110045 0.0 mean_ko H3K9ac 0.5592674641511871 0.0 mean_ko mean_wt 0.9918038831526816 0.0 mean_ko mean_ko 1.0 0.0 mean_ko mean_ko-wt -0.3900643761493835 0.0 mean_ko H3K4me1 0.11010738724216297 8.198213479177807e-36 mean_ko H3K4me2 0.17946752733713214 4.2877666356691384e-93 mean_ko H3K4me3 0.5881743020108969 0.0 mean_ko H3K27ac 0.5590745553867866 0.0 mean_ko H3K36me3 0.5020241405110045 0.0 mean_ko H3K27me3 -0.21440371983804937 5.856222186263505e-133 mean_ko Log2FC FB -0.3652333864195841 0.0 mean_ko Log2FC MB -0.403537786657164 0.0 mean_ko Log2FC HB -0.44133074189202925 0.0 mean_ko Log2FC SC -0.263675332447768 1.645195278633999e-202 mean_ko VAE0 0.6344396714685748 0.0 mean_ko VAE1 -0.1719717780022099 1.59630783090709e-85 mean_ko VAE2 -0.7019731621801953 0.0 mean_ko-wt H3K9me3 0.07171376981069016 4.577998673738638e-16 mean_ko-wt H3K4me1 -0.12351444023102398 1.0993490972728222e-44 mean_ko-wt H3K4me2 -0.09888737471837038 3.5238851765166497e-29 mean_ko-wt H3K4me3 -0.48552252459057177 0.0 mean_ko-wt H3K27ac -0.42205738337847143 0.0 mean_ko-wt H3K36me3 -0.48511612572627455 0.0 mean_ko-wt H3K9ac -0.39977752635279323 0.0 mean_ko-wt mean_wt -0.4845025698158839 0.0 mean_ko-wt mean_ko -0.3900643761493835 0.0 mean_ko-wt mean_ko-wt 1.0 0.0 mean_ko-wt H3K4me1 -0.12351444023102398 1.0993490972728222e-44 mean_ko-wt H3K4me2 -0.09888737471837038 3.5238851765166497e-29 mean_ko-wt H3K4me3 -0.48552252459057177 0.0 mean_ko-wt H3K27ac -0.42205738337847143 0.0 mean_ko-wt H3K36me3 -0.48511612572627455 0.0 mean_ko-wt H3K27me3 0.4103386737863612 0.0 mean_ko-wt Log2FC FB 0.8805965114989298 0.0 mean_ko-wt Log2FC MB 0.8704536519772174 0.0 mean_ko-wt Log2FC HB 0.7455835313610006 0.0 mean_ko-wt Log2FC SC 0.6978367483354114 0.0 mean_ko-wt VAE0 -0.07009363106114355 2.0505434236585485e-15 mean_ko-wt VAE1 -0.3207239010421294 5.667786389040793e-304 mean_ko-wt VAE2 0.5486240945696328 0.0 H3K4me1 H3K9me3 -0.14322560612182772 1.2682004430470273e-59 H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K9ac 0.2614374133944155 5.329955864994627e-199 H3K4me1 mean_wt 0.11747411642321601 1.4678426946564922e-40 H3K4me1 mean_ko 0.11010738724216297 8.198213479177807e-36 H3K4me1 mean_ko-wt -0.12351444023102397 1.0993490972728222e-44 H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K27me3 0.03880145459733418 1.1304167715365197e-05 H3K4me1 Log2FC FB -0.14880590787152698 2.8859404622978633e-64 H3K4me1 Log2FC MB -0.10387373575786396 4.872008964191265e-32 H3K4me1 Log2FC HB -0.05777615219411477 6.12378414825415e-11 H3K4me1 Log2FC SC -0.07950151137451965 2.1197279496874784e-19 H3K4me1 VAE0 -0.0446884001956768 4.251104865461492e-07 H3K4me1 VAE1 -0.08406049569917753 1.6476006172029343e-21 H3K4me1 VAE2 -0.19398340219121096 9.81715077576278e-109 H3K4me2 H3K9me3 -0.012727232249096996 0.14996053879600038 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K9ac 0.468787932774562 0.0 H3K4me2 mean_wt 0.18397384400291664 8.235251256534976e-98 H3K4me2 mean_ko 0.17946752733713214 4.2877666356691384e-93 H3K4me2 mean_ko-wt -0.09888737471837038 3.5238851765166497e-29 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K27me3 0.1836859102996935 1.6625204625607636e-97 H3K4me2 Log2FC FB -0.12969526458358954 3.9896551047753745e-49 H3K4me2 Log2FC MB -0.09642120978180409 8.108800676805556e-28 H3K4me2 Log2FC HB -0.10550208768149451 5.2879602412044856e-33 H3K4me2 Log2FC SC -0.08178812465927765 1.9179743858493656e-20 H3K4me2 VAE0 0.08834956848013696 1.335708453202751e-23 H3K4me2 VAE1 -0.2636875996916555 1.5735477679806943e-202 H3K4me2 VAE2 -0.18498827221190547 6.866667382156619e-99 H3K4me3 H3K9me3 -0.031174294277473535 0.0004201886170928379 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K9ac 0.8372246669947291 0.0 H3K4me3 mean_wt 0.6172021315146129 0.0 H3K4me3 mean_ko 0.588174302010897 0.0 H3K4me3 mean_ko-wt -0.48552252459057177 0.0 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K27me3 -0.28958561139696254 1.0906807671975684e-245 H3K4me3 Log2FC FB -0.4470506488127457 0.0 H3K4me3 Log2FC MB -0.4473702882601094 0.0 H3K4me3 Log2FC HB -0.41979679249095553 0.0 H3K4me3 Log2FC SC -0.3297606127199917 0.0 H3K4me3 VAE0 0.30597343061378124 1.8027240014084392e-275 H3K4me3 VAE1 0.07296409905287944 1.4064868617096597e-16 H3K4me3 VAE2 -0.5298862221619517 0.0 H3K27ac H3K9me3 -0.00358288782217438 0.6852778158564187 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K9ac 0.7602537276873533 0.0 H3K27ac mean_wt 0.5827182905478256 0.0 H3K27ac mean_ko 0.5590745553867865 0.0 H3K27ac mean_ko-wt -0.42205738337847143 0.0 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K27me3 -0.2526689336918533 1.4216280599037781e-185 H3K27ac Log2FC FB -0.3815669720025924 0.0 H3K27ac Log2FC MB -0.42208837018425827 0.0 H3K27ac Log2FC HB -0.408298389874337 0.0 H3K27ac Log2FC SC -0.2977765740809075 2.491982785246384e-260 H3K27ac VAE0 0.3514588028990406 0.0 H3K27ac VAE1 0.05627016617662167 1.8912472752821038e-10 H3K27ac VAE2 -0.4482816481694506 0.0 H3K36me3 H3K9me3 -0.08596619953337728 1.9975762016514672e-22 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K9ac 0.46303044184726333 0.0 H3K36me3 mean_wt 0.534749603678121 0.0 H3K36me3 mean_ko 0.5020241405110045 0.0 H3K36me3 mean_ko-wt -0.4851161257262746 0.0 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K27me3 -0.35415360112947697 0.0 H3K36me3 Log2FC FB -0.4744138412763159 0.0 H3K36me3 Log2FC MB -0.430059873023584 0.0 H3K36me3 Log2FC HB -0.3634108408611482 0.0 H3K36me3 Log2FC SC -0.33209079328572233 0.0 H3K36me3 VAE0 0.20757601112819188 1.4444036902704797e-124 H3K36me3 VAE1 0.18686624881923297 6.647406888366817e-101 H3K36me3 VAE2 -0.5117903743601302 0.0 H3K27me3 H3K9me3 0.06719198878685362 2.764988245960282e-14 H3K27me3 H3K4me1 0.03880145459733418 1.1304167715365197e-05 H3K27me3 H3K4me2 0.18368591029969353 1.662520462560716e-97 H3K27me3 H3K4me3 -0.2895856113969625 1.0906807671978166e-245 H3K27me3 H3K27ac -0.2526689336918533 1.4216280599037781e-185 H3K27me3 H3K36me3 -0.35415360112947697 0.0 H3K27me3 H3K9ac -0.2049746737349427 1.9027988176860502e-121 H3K27me3 mean_wt -0.25633608266022023 3.991995123962663e-191 H3K27me3 mean_ko -0.21440371983804937 5.856222186263505e-133 H3K27me3 mean_ko-wt 0.4103386737863612 0.0 H3K27me3 H3K4me1 0.03880145459733418 1.1304167715365197e-05 H3K27me3 H3K4me2 0.18368591029969353 1.662520462560716e-97 H3K27me3 H3K4me3 -0.2895856113969625 1.0906807671978166e-245 H3K27me3 H3K27ac -0.2526689336918533 1.4216280599037781e-185 H3K27me3 H3K36me3 -0.35415360112947697 0.0 H3K27me3 H3K27me3 1.0 0.0 H3K27me3 Log2FC FB 0.37643342244583206 0.0 H3K27me3 Log2FC MB 0.3225460106810916 1.314030964712288e-307 H3K27me3 Log2FC HB 0.2347875537574898 8.590917791342444e-160 H3K27me3 Log2FC SC 0.2956826706138225 1.5329233866307224e-256 H3K27me3 VAE0 -0.10520428908479008 7.957836150465658e-33 H3K27me3 VAE1 -0.8608600491616873 0.0 H3K27me3 VAE2 0.2279197055845889 1.8859458409143461e-150 Log2FC FB H3K9me3 0.09824918653348805 7.994118616748979e-29 Log2FC FB H3K4me1 -0.14880590787152698 2.8859404622978633e-64 Log2FC FB H3K4me2 -0.12969526458358954 3.9896551047753745e-49 Log2FC FB H3K4me3 -0.4470506488127457 0.0 Log2FC FB H3K27ac -0.3815669720025924 0.0 Log2FC FB H3K36me3 -0.4744138412763159 0.0 Log2FC FB H3K9ac -0.36769026751822986 0.0 Log2FC FB mean_wt -0.4478380069404565 0.0 Log2FC FB mean_ko -0.3652333864195841 0.0 Log2FC FB mean_ko-wt 0.8805965114989297 0.0 Log2FC FB H3K4me1 -0.14880590787152698 2.8859404622978633e-64 Log2FC FB H3K4me2 -0.12969526458358954 3.9896551047753745e-49 Log2FC FB H3K4me3 -0.4470506488127457 0.0 Log2FC FB H3K27ac -0.3815669720025924 0.0 Log2FC FB H3K36me3 -0.4744138412763159 0.0 Log2FC FB H3K27me3 0.37643342244583206 0.0 Log2FC FB Log2FC FB 1.0 0.0 Log2FC FB Log2FC MB 0.7040491449224625 0.0 Log2FC FB Log2FC HB 0.5367783316680511 0.0 Log2FC FB Log2FC SC 0.5730464144717174 0.0 Log2FC FB VAE0 0.0035828141346172554 0.6852839419752368 Log2FC FB VAE1 -0.3026340104905353 3.046326192390388e-269 Log2FC FB VAE2 0.5758155364107505 0.0 Log2FC MB H3K9me3 0.019587856642572236 0.02670181757210075 Log2FC MB H3K4me1 -0.10387373575786396 4.872008964191265e-32 Log2FC MB H3K4me2 -0.09642120978180409 8.108800676805556e-28 Log2FC MB H3K4me3 -0.44737028826010944 0.0 Log2FC MB H3K27ac -0.4220883701842583 0.0 Log2FC MB H3K36me3 -0.43005987302358406 0.0 Log2FC MB H3K9ac -0.3901357221445141 0.0 Log2FC MB mean_wt -0.4838366012597243 0.0 Log2FC MB mean_ko -0.40353778665716405 0.0 Log2FC MB mean_ko-wt 0.8704536519772174 0.0 Log2FC MB H3K4me1 -0.10387373575786396 4.872008964191265e-32 Log2FC MB H3K4me2 -0.09642120978180409 8.108800676805556e-28 Log2FC MB H3K4me3 -0.44737028826010944 0.0 Log2FC MB H3K27ac -0.4220883701842583 0.0 Log2FC MB H3K36me3 -0.43005987302358406 0.0 Log2FC MB H3K27me3 0.32254601068109157 1.314030964712587e-307 Log2FC MB Log2FC FB 0.7040491449224625 0.0 Log2FC MB Log2FC MB 0.9999999999999999 0.0 Log2FC MB Log2FC HB 0.7578212671301587 0.0 Log2FC MB Log2FC SC 0.5751592030701783 0.0 Log2FC MB VAE0 -0.17513450220728732 1.1229971545114095e-88 Log2FC MB VAE1 -0.21321910758293916 1.757463064410544e-131 Log2FC MB VAE2 0.46938929025701914 0.0 Log2FC HB H3K9me3 -0.008660581682350359 0.32726345641777266 Log2FC HB H3K4me1 -0.05777615219411477 6.12378414825415e-11 Log2FC HB H3K4me2 -0.10550208768149451 5.2879602412044856e-33 Log2FC HB H3K4me3 -0.4197967924909555 0.0 Log2FC HB H3K27ac -0.408298389874337 0.0 Log2FC HB H3K36me3 -0.36341084086114817 0.0 Log2FC HB H3K9ac -0.3936885591832504 0.0 Log2FC HB mean_wt -0.506836755731281 0.0 Log2FC HB mean_ko -0.4413307418920293 0.0 Log2FC HB mean_ko-wt 0.7455835313610005 0.0 Log2FC HB H3K4me1 -0.05777615219411477 6.12378414825415e-11 Log2FC HB H3K4me2 -0.10550208768149451 5.2879602412044856e-33 Log2FC HB H3K4me3 -0.4197967924909555 0.0 Log2FC HB H3K27ac -0.408298389874337 0.0 Log2FC HB H3K36me3 -0.36341084086114817 0.0 Log2FC HB H3K27me3 0.2347875537574898 8.590917791342444e-160 Log2FC HB Log2FC FB 0.5367783316680511 0.0 Log2FC HB Log2FC MB 0.7578212671301588 0.0 Log2FC HB Log2FC HB 1.0 0.0 Log2FC HB Log2FC SC 0.47172347343211496 0.0 Log2FC HB VAE0 -0.3982180606696101 0.0 Log2FC HB VAE1 -0.07716379044745166 2.3055509300983664e-18 Log2FC HB VAE2 0.2883184296728832 1.8180376139160885e-243 Log2FC SC H3K9me3 0.02561550423527578 0.0037565089010411353 Log2FC SC H3K4me1 -0.07950151137451963 2.1197279496875092e-19 Log2FC SC H3K4me2 -0.08178812465927764 1.9179743858494207e-20 Log2FC SC H3K4me3 -0.3297606127199917 0.0 Log2FC SC H3K27ac -0.2977765740809076 2.4919827852461e-260 Log2FC SC H3K36me3 -0.33209079328572233 0.0 Log2FC SC H3K9ac -0.26251104559696414 1.1139140119303159e-200 Log2FC SC mean_wt -0.3293862683906489 0.0 Log2FC SC mean_ko -0.263675332447768 1.645195278633999e-202 Log2FC SC mean_ko-wt 0.6978367483354115 0.0 Log2FC SC H3K4me1 -0.07950151137451963 2.1197279496875092e-19 Log2FC SC H3K4me2 -0.08178812465927764 1.9179743858494207e-20 Log2FC SC H3K4me3 -0.3297606127199917 0.0 Log2FC SC H3K27ac -0.2977765740809076 2.4919827852461e-260 Log2FC SC H3K36me3 -0.33209079328572233 0.0 Log2FC SC H3K27me3 0.2956826706138225 1.5329233866307224e-256 Log2FC SC Log2FC FB 0.5730464144717174 0.0 Log2FC SC Log2FC MB 0.5751592030701783 0.0 Log2FC SC Log2FC HB 0.47172347343211496 0.0 Log2FC SC Log2FC SC 0.9999999999999999 0.0 Log2FC SC VAE0 0.04095923910797624 3.570096858074452e-06 Log2FC SC VAE1 -0.24496989653146614 3.2652391791136445e-174 Log2FC SC VAE2 0.4631936937538506 0.0 VAE0 H3K9me3 0.09476451187011106 6.375261288317115e-27 VAE0 H3K4me1 -0.0446884001956768 4.251104865461492e-07 VAE0 H3K4me2 0.08834956848013696 1.335708453202751e-23 VAE0 H3K4me3 0.30597343061378124 1.8027240014084392e-275 VAE0 H3K27ac 0.3514588028990406 0.0 VAE0 H3K36me3 0.20757601112819188 1.4444036902704797e-124 VAE0 H3K9ac 0.3679083251474569 0.0 VAE0 mean_wt 0.6150030977542656 0.0 VAE0 mean_ko 0.6344396714685748 0.0 VAE0 mean_ko-wt -0.07009363106114355 2.0505434236585485e-15 VAE0 H3K4me1 -0.0446884001956768 4.251104865461492e-07 VAE0 H3K4me2 0.08834956848013696 1.335708453202751e-23 VAE0 H3K4me3 0.30597343061378124 1.8027240014084392e-275 VAE0 H3K27ac 0.3514588028990406 0.0 VAE0 H3K36me3 0.20757601112819188 1.4444036902704797e-124 VAE0 H3K27me3 -0.10520428908479008 7.957836150465658e-33 VAE0 Log2FC FB 0.003582814134617256 0.6852839419752368 VAE0 Log2FC MB -0.17513450220728735 1.1229971545113458e-88 VAE0 Log2FC HB -0.39821806066961 0.0 VAE0 Log2FC SC 0.04095923910797624 3.570096858074452e-06 VAE0 VAE0 1.0 0.0 VAE0 VAE1 -0.17727771491123703 7.574731590825203e-91 VAE0 VAE2 0.024102462909318584 0.006397261512370033 VAE1 H3K9me3 -0.07756915894577787 1.5319117975903234e-18 VAE1 H3K4me1 -0.08406049569917753 1.6476006172029343e-21 VAE1 H3K4me2 -0.2636875996916555 1.5735477679806943e-202 VAE1 H3K4me3 0.07296409905287946 1.4064868617096594e-16 VAE1 H3K27ac 0.05627016617662167 1.8912472752821038e-10 VAE1 H3K36me3 0.18686624881923297 6.647406888366817e-101 VAE1 H3K9ac 0.0037976716291406483 0.6675108445696198 VAE1 mean_wt -0.11951742691063598 6.232716507282639e-42 VAE1 mean_ko -0.1719717780022099 1.59630783090709e-85 VAE1 mean_ko-wt -0.3207239010421294 5.667786389040793e-304 VAE1 H3K4me1 -0.08406049569917753 1.6476006172029343e-21 VAE1 H3K4me2 -0.2636875996916555 1.5735477679806943e-202 VAE1 H3K4me3 0.07296409905287946 1.4064868617096594e-16 VAE1 H3K27ac 0.05627016617662167 1.8912472752821038e-10 VAE1 H3K36me3 0.18686624881923297 6.647406888366817e-101 VAE1 H3K27me3 -0.8608600491616872 0.0 VAE1 Log2FC FB -0.30263401049053534 3.046326192389002e-269 VAE1 Log2FC MB -0.2132191075829392 1.757463064410544e-131 VAE1 Log2FC HB -0.07716379044745164 2.3055509300984496e-18 VAE1 Log2FC SC -0.2449698965314661 3.2652391791142007e-174 VAE1 VAE0 -0.17727771491123703 7.574731590825203e-91 VAE1 VAE1 1.0 0.0 VAE1 VAE2 0.015624110732809882 0.077162199979443 VAE2 H3K9me3 0.08363311818464449 2.6275582494385576e-21 VAE2 H3K4me1 -0.19398340219121096 9.81715077576278e-109 VAE2 H3K4me2 -0.18498827221190545 6.86666738215701e-99 VAE2 H3K4me3 -0.5298862221619517 0.0 VAE2 H3K27ac -0.4482816481694506 0.0 VAE2 H3K36me3 -0.5117903743601302 0.0 VAE2 H3K9ac -0.42709438615274053 0.0 VAE2 mean_wt -0.7268051753065692 0.0 VAE2 mean_ko -0.7019731621801955 0.0 VAE2 mean_ko-wt 0.5486240945696327 0.0 VAE2 H3K4me1 -0.19398340219121096 9.81715077576278e-109 VAE2 H3K4me2 -0.18498827221190545 6.86666738215701e-99 VAE2 H3K4me3 -0.5298862221619517 0.0 VAE2 H3K27ac -0.4482816481694506 0.0 VAE2 H3K36me3 -0.5117903743601302 0.0 VAE2 H3K27me3 0.2279197055845889 1.8859458409143461e-150 VAE2 Log2FC FB 0.5758155364107505 0.0 VAE2 Log2FC MB 0.46938929025701914 0.0 VAE2 Log2FC HB 0.2883184296728831 1.8180376139162956e-243 VAE2 Log2FC SC 0.4631936937538506 0.0 VAE2 VAE0 0.024102462909318584 0.006397261512370033 VAE2 VAE1 0.015624110732809882 0.077162199979443 VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K27me3 0.03880145459733418 1.1304167715365197e-05 H3K4me1 Log2FC FB -0.14880590787152698 2.8859404622978633e-64 H3K4me1 Log2FC MB -0.10387373575786396 4.872008964191265e-32 H3K4me1 Log2FC HB -0.05777615219411477 6.12378414825415e-11 H3K4me1 Log2FC SC -0.07950151137451965 2.1197279496874784e-19 H3K4me1 VAE0 -0.0446884001956768 4.251104865461492e-07 H3K4me1 VAE1 -0.08406049569917753 1.6476006172029343e-21 H3K4me1 VAE2 -0.19398340219121096 9.81715077576278e-109 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K27me3 0.1836859102996935 1.6625204625607636e-97 H3K4me2 Log2FC FB -0.12969526458358954 3.9896551047753745e-49 H3K4me2 Log2FC MB -0.09642120978180409 8.108800676805556e-28 H3K4me2 Log2FC HB -0.10550208768149451 5.2879602412044856e-33 H3K4me2 Log2FC SC -0.08178812465927765 1.9179743858493656e-20 H3K4me2 VAE0 0.08834956848013696 1.335708453202751e-23 H3K4me2 VAE1 -0.2636875996916555 1.5735477679806943e-202 H3K4me2 VAE2 -0.18498827221190547 6.866667382156619e-99 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K27me3 -0.28958561139696254 1.0906807671975684e-245 H3K4me3 Log2FC FB -0.4470506488127457 0.0 H3K4me3 Log2FC MB -0.4473702882601094 0.0 H3K4me3 Log2FC HB -0.41979679249095553 0.0 H3K4me3 Log2FC SC -0.3297606127199917 0.0 H3K4me3 VAE0 0.30597343061378124 1.8027240014084392e-275 H3K4me3 VAE1 0.07296409905287944 1.4064868617096597e-16 H3K4me3 VAE2 -0.5298862221619517 0.0 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K27me3 -0.2526689336918533 1.4216280599037781e-185 H3K27ac Log2FC FB -0.3815669720025924 0.0 H3K27ac Log2FC MB -0.42208837018425827 0.0 H3K27ac Log2FC HB -0.408298389874337 0.0 H3K27ac Log2FC SC -0.2977765740809075 2.491982785246384e-260 H3K27ac VAE0 0.3514588028990406 0.0 H3K27ac VAE1 0.05627016617662167 1.8912472752821038e-10 H3K27ac VAE2 -0.4482816481694506 0.0 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K27me3 -0.35415360112947697 0.0 H3K36me3 Log2FC FB -0.4744138412763159 0.0 H3K36me3 Log2FC MB -0.430059873023584 0.0 H3K36me3 Log2FC HB -0.3634108408611482 0.0 H3K36me3 Log2FC SC -0.33209079328572233 0.0 H3K36me3 VAE0 0.20757601112819188 1.4444036902704797e-124 H3K36me3 VAE1 0.18686624881923297 6.647406888366817e-101 H3K36me3 VAE2 -0.5117903743601302 0.0 H3K27me3 H3K4me1 0.03880145459733418 1.1304167715365197e-05 H3K27me3 H3K4me2 0.18368591029969353 1.662520462560716e-97 H3K27me3 H3K4me3 -0.2895856113969625 1.0906807671978166e-245 H3K27me3 H3K27ac -0.2526689336918533 1.4216280599037781e-185 H3K27me3 H3K36me3 -0.35415360112947697 0.0 H3K27me3 H3K27me3 1.0 0.0 H3K27me3 Log2FC FB 0.37643342244583206 0.0 H3K27me3 Log2FC MB 0.3225460106810916 1.314030964712288e-307 H3K27me3 Log2FC HB 0.2347875537574898 8.590917791342444e-160 H3K27me3 Log2FC SC 0.2956826706138225 1.5329233866307224e-256 H3K27me3 VAE0 -0.10520428908479008 7.957836150465658e-33 H3K27me3 VAE1 -0.8608600491616873 0.0 H3K27me3 VAE2 0.2279197055845889 1.8859458409143461e-150 Log2FC FB H3K4me1 -0.14880590787152698 2.8859404622978633e-64 Log2FC FB H3K4me2 -0.12969526458358954 3.9896551047753745e-49 Log2FC FB H3K4me3 -0.4470506488127457 0.0 Log2FC FB H3K27ac -0.3815669720025924 0.0 Log2FC FB H3K36me3 -0.4744138412763159 0.0 Log2FC FB H3K27me3 0.37643342244583206 0.0 Log2FC FB Log2FC FB 1.0 0.0 Log2FC FB Log2FC MB 0.7040491449224625 0.0 Log2FC FB Log2FC HB 0.5367783316680511 0.0 Log2FC FB Log2FC SC 0.5730464144717174 0.0 Log2FC FB VAE0 0.0035828141346172554 0.6852839419752368 Log2FC FB VAE1 -0.3026340104905353 3.046326192390388e-269 Log2FC FB VAE2 0.5758155364107505 0.0 Log2FC MB H3K4me1 -0.10387373575786396 4.872008964191265e-32 Log2FC MB H3K4me2 -0.09642120978180409 8.108800676805556e-28 Log2FC MB H3K4me3 -0.44737028826010944 0.0 Log2FC MB H3K27ac -0.4220883701842583 0.0 Log2FC MB H3K36me3 -0.43005987302358406 0.0 Log2FC MB H3K27me3 0.32254601068109157 1.314030964712587e-307 Log2FC MB Log2FC FB 0.7040491449224625 0.0 Log2FC MB Log2FC MB 0.9999999999999999 0.0 Log2FC MB Log2FC HB 0.7578212671301587 0.0 Log2FC MB Log2FC SC 0.5751592030701783 0.0 Log2FC MB VAE0 -0.17513450220728732 1.1229971545114095e-88 Log2FC MB VAE1 -0.21321910758293916 1.757463064410544e-131 Log2FC MB VAE2 0.46938929025701914 0.0 Log2FC HB H3K4me1 -0.05777615219411477 6.12378414825415e-11 Log2FC HB H3K4me2 -0.10550208768149451 5.2879602412044856e-33 Log2FC HB H3K4me3 -0.4197967924909555 0.0 Log2FC HB H3K27ac -0.408298389874337 0.0 Log2FC HB H3K36me3 -0.36341084086114817 0.0 Log2FC HB H3K27me3 0.2347875537574898 8.590917791342444e-160 Log2FC HB Log2FC FB 0.5367783316680511 0.0 Log2FC HB Log2FC MB 0.7578212671301588 0.0 Log2FC HB Log2FC HB 1.0 0.0 Log2FC HB Log2FC SC 0.47172347343211496 0.0 Log2FC HB VAE0 -0.3982180606696101 0.0 Log2FC HB VAE1 -0.07716379044745166 2.3055509300983664e-18 Log2FC HB VAE2 0.2883184296728832 1.8180376139160885e-243 Log2FC SC H3K4me1 -0.07950151137451963 2.1197279496875092e-19 Log2FC SC H3K4me2 -0.08178812465927764 1.9179743858494207e-20 Log2FC SC H3K4me3 -0.3297606127199917 0.0 Log2FC SC H3K27ac -0.2977765740809076 2.4919827852461e-260 Log2FC SC H3K36me3 -0.33209079328572233 0.0 Log2FC SC H3K27me3 0.2956826706138225 1.5329233866307224e-256 Log2FC SC Log2FC FB 0.5730464144717174 0.0 Log2FC SC Log2FC MB 0.5751592030701783 0.0 Log2FC SC Log2FC HB 0.47172347343211496 0.0 Log2FC SC Log2FC SC 0.9999999999999999 0.0 Log2FC SC VAE0 0.04095923910797624 3.570096858074452e-06 Log2FC SC VAE1 -0.24496989653146614 3.2652391791136445e-174 Log2FC SC VAE2 0.4631936937538506 0.0 VAE0 H3K4me1 -0.0446884001956768 4.251104865461492e-07 VAE0 H3K4me2 0.08834956848013696 1.335708453202751e-23 VAE0 H3K4me3 0.30597343061378124 1.8027240014084392e-275 VAE0 H3K27ac 0.3514588028990406 0.0 VAE0 H3K36me3 0.20757601112819188 1.4444036902704797e-124 VAE0 H3K27me3 -0.10520428908479008 7.957836150465658e-33 VAE0 Log2FC FB 0.003582814134617256 0.6852839419752368 VAE0 Log2FC MB -0.17513450220728735 1.1229971545113458e-88 VAE0 Log2FC HB -0.39821806066961 0.0 VAE0 Log2FC SC 0.04095923910797624 3.570096858074452e-06 VAE0 VAE0 1.0 0.0 VAE0 VAE1 -0.17727771491123703 7.574731590825203e-91 VAE0 VAE2 0.024102462909318584 0.006397261512370033 VAE1 H3K4me1 -0.08406049569917753 1.6476006172029343e-21 VAE1 H3K4me2 -0.2636875996916555 1.5735477679806943e-202 VAE1 H3K4me3 0.07296409905287946 1.4064868617096594e-16 VAE1 H3K27ac 0.05627016617662167 1.8912472752821038e-10 VAE1 H3K36me3 0.18686624881923297 6.647406888366817e-101 VAE1 H3K27me3 -0.8608600491616872 0.0 VAE1 Log2FC FB -0.30263401049053534 3.046326192389002e-269 VAE1 Log2FC MB -0.2132191075829392 1.757463064410544e-131 VAE1 Log2FC HB -0.07716379044745164 2.3055509300984496e-18 VAE1 Log2FC SC -0.2449698965314661 3.2652391791142007e-174 VAE1 VAE0 -0.17727771491123703 7.574731590825203e-91 VAE1 VAE1 1.0 0.0 VAE1 VAE2 0.015624110732809882 0.077162199979443 VAE2 H3K4me1 -0.19398340219121096 9.81715077576278e-109 VAE2 H3K4me2 -0.18498827221190545 6.86666738215701e-99 VAE2 H3K4me3 -0.5298862221619517 0.0 VAE2 H3K27ac -0.4482816481694506 0.0 VAE2 H3K36me3 -0.5117903743601302 0.0 VAE2 H3K27me3 0.2279197055845889 1.8859458409143461e-150 VAE2 Log2FC FB 0.5758155364107505 0.0 VAE2 Log2FC MB 0.46938929025701914 0.0 VAE2 Log2FC HB 0.2883184296728831 1.8180376139162956e-243 VAE2 Log2FC SC 0.4631936937538506 0.0 VAE2 VAE0 0.024102462909318584 0.006397261512370033 VAE2 VAE1 0.015624110732809882 0.077162199979443 VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
external_gene_name wt11fb_merged-rep wt13fb_merged-rep wt15fb_merged-rep \ 0 Foxg1 8.161763 9.036692 9.295708 1 En2 0.494674 0.168652 0.353709 2 Sox3 7.329884 7.220449 5.700304 3 Hoxc9 0.070489 0.020139 0.114239 wt18fb_merged-rep wt11mb_merged-rep wt13mb_merged-rep wt15mb_merged-rep \ 0 9.176002 3.555796 0.152191 0.326289 1 0.413756 7.499362 8.031306 7.013888 2 4.480171 7.569871 6.390573 4.469231 3 0.111092 0.071978 0.022155 0.032423 wt18mb_merged-rep wt11hb_merged-rep ... ko15mb_merged-rep \ 0 1.557369 2.814731 ... 4.962096 1 6.668011 6.872836 ... 6.280874 2 4.535570 7.260694 ... 3.079978 3 0.016071 0.118288 ... 4.920412 ko18mb_merged-rep ko11hb_merged-rep ko13hb_merged-rep ko15hb_merged-rep \ 0 5.039551 5.698836 4.590381 4.688194 1 6.268489 4.803762 5.565673 6.119783 2 3.464150 7.339934 3.056591 2.845606 3 4.175629 2.662632 5.024299 4.750092 ko18hb_merged-rep ko11sc_merged-rep ko13sc_merged-rep ko15sc_merged-rep \ 0 4.667710 1.161890 3.216371 3.642766 1 4.860775 3.372272 5.201905 4.665178 2 2.955592 6.693111 3.609604 3.244365 3 4.168916 6.278640 5.842700 5.185208 ko18sc_merged-rep 0 3.737086 1 4.312455 2 3.485175 3 5.005286 [4 rows x 33 columns]
external_gene_name wt11fb1 wt11fb2 wt13fb1 wt13fb2 wt15fb1 \
0 Foxg1 0.396663 0.415054 0.339704 0.338129 0.240128
1 En2 0.299162 0.302716 0.297276 0.293493 0.241776
2 Sox3 0.570532 0.566920 0.516674 0.512157 0.420147
3 Hoxc9 0.068199 0.063579 0.100579 0.111775 0.115555
wt15fb2 wt18fb1 wt18fb2 wt11mb1 ... \
0 0.239497 0.215054 0.225889 0.378798 ...
1 0.235451 0.239219 0.240315 0.310785 ...
2 0.406546 0.335557 0.375286 0.552104 ...
3 0.118861 0.130621 0.127169 0.100477 ...
hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal_log2 \
0 0.821710
1 0.839977
2 0.200723
3 1.000000
hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal_log2 \
0 0.803528
1 0.802012
2 0.301032
3 0.997743
hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal_log2 \
0 0.715712
1 0.792974
2 0.019209
3 1.000000
midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal_log2 \
0 0.714856
1 0.839047
2 0.150847
3 1.000000
midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal_log2 \
0 0.669096
1 0.812188
2 0.018569
3 1.000000
midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal_log2 \
0 0.700308
1 0.793829
2 0.322843
3 1.000000
midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal_log2 \
0 0.747783
1 0.812979
2 0.390880
3 1.000000
midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal_log2 \
0 0.816992
1 0.834253
2 0.191405
3 1.000000
midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal_log2 \
0 0.779879
1 0.804545
2 0.318443
3 0.992082
midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal_log2
0 0.755980
1 0.772538
2 0.117959
3 1.000000
[4 rows x 98 columns]
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. self.fig.tight_layout(**tight_params)
external_gene_name VAE0 VAE1 VAE2 0 Foxg1 -1.992226 -1.512505 -0.965825 1 En2 -1.350005 -2.014320 -0.434887 2 Sox3 -1.340308 -0.134940 -1.315866 3 Hoxc9 -1.353282 -3.168541 0.372992
ko_cols = [c for c in df_input.columns if 'ko' in c]
wt_cols = [c for c in df_input.columns if 'wt' in c]
# get IDs of the KO cols
ko_col_ids = []
for i, c in enumerate(df_input.columns):
if 'ko' in c:
ko_col_ids.append(i)
bin_cmap = 'bone'
# Plot the input space WT and KO
merged_wt = [c for c in merged_cols if 'wt' in c]
merged_ko = [c for c in merged_cols if 'ko' in c]
mb_genes = ['En1', 'En2', 'Lmx1a', 'Otx2', 'Sall4']
plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes,
merged_wt, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=True, cmap=bin_cmap,
method='input', title=f'All markers input WT {experiment_name}')
plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes,
merged_ko, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=True, cmap=bin_cmap,
method='input', title=f'All markers input KO {experiment_name}')
# Plot the decoding space for KO and then WT
plot_gene_heatmap(df_input, fb_genes, mb_genes, hb_genes, sc_genes,
wt_cols, all_vae_data, vae_mse,
gene_name, merge_reps=True, mark='H3K27me3', cmap=bin_cmap,
method='decoding', title=f'All markers decoding WT {experiment_name}')
# Plot the latent space
plot_gene_heatmap(df, fb_genes, mb_genes, hb_genes, sc_genes,
['VAE0', 'VAE1', 'VAE2'], vae_data, vae_mse,
gene_name,
cmap=div_cmap,
mark='H3K27me3', method='latent', title=f'All markers latent {experiment_name}')
progenitors = ['Sox2', 'Sox1', 'Sox3', 'Hes1', 'Hes5']
neurons = ['Snap25', 'Syt1', 'Slc32a1','Slc17a6', 'Syn1']
glia = ['Cspg4', 'Aqp4', 'Slc6a11', 'Olig1', 'Igfbp3']
# Plot the input space
plot_gene_heatmap(df, progenitors, neurons, glia, [],
merged_wt, vae_data, vae_mse,
gene_name, mark='H3K27me3', method='input', cmap=bin_cmap,
merge_reps=True, title=f'Time markers input WT {experiment_name}')
plot_gene_heatmap(df, progenitors, neurons, glia, [],
merged_ko, vae_data, vae_mse,
gene_name, mark='H3K27me3', method='input', cmap=bin_cmap,
merge_reps=True, title=f'Time markers input KO {experiment_name}')
# Plot the decoding space
plot_gene_heatmap(df_input, progenitors, neurons, glia, [],
wt_cols, vae_data, vae_mse,
gene_name, mark='H3K27me3', method='decoding', cmap=bin_cmap,
merge_reps=True, title=f'Time markers decoding WT {experiment_name}')
# Plot the latent space
plot_gene_heatmap(df, progenitors, neurons, glia, [],
['VAE0', 'VAE1', 'VAE2'], vae_data, vae_mse,
gene_name, mark='H3K27me3', method='latent',
cmap=div_cmap, title=f'Time markers latent {experiment_name}')
# -----------------------------------------------------------------------------------
# Plot the genes on a scatter plot
# -----------------------------------------------------------------------------------
gene_markers_ns = [['Slc32a1', 'Gad1', 'Gad2', 'Slc6a1'],
['Slc17a6', 'VGLUT2', 'Slc17a7', 'VGLUT1', 'Slc17a8', 'VGLUT3', 'Slc1a1', 'Slc1a2', 'Slc1a6'],
['Chat', 'Slc5a7', 'Slc18a3', 'Ache'],
['Tubb3', 'Snap25', 'Syt1'],
['Gfap', 'Olig2'],
['Sox2', 'Hes1', 'Hes5', 'Vim']]
marker_labels_ns = ['NS GABAergic', 'NS Glutamatergic', 'NS Cholinergic', 'NS Neurons', 'NS Glia', 'NS Progenitors']
vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_ns,
gene_markers_ns, output_dir=fig_dir, plt_bg=False,vae_data=vae_data,
title=f'Markers NS {experiment_name}', fig_type="pdf",
show_plt=True, save_fig=True)
gene_markers_sep = [['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6'],
['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4'],
['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b'],
['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11',
'Hoxa13', 'Hoxb9', 'Hoxb13', 'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13'],
['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a',
'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a'],
['Cdkn1a', 'Cdkn1b', 'Cdkn1c', 'Cdkn2a', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d'],
['Sox2', 'Sox1', 'Sox3', 'Hes1', 'Hes5'],
['Snap25', 'Syt1', 'Slc32a1','Slc17a6', 'Syn1'],
['Cspg4', 'Aqp4', 'Slc6a11', 'Olig1', 'Igfbp3'],
['Foxg1'],
['En2'],
['Phox2b'],
['Hoxc9'],
['Sox3']
]
marker_labels_sep = ['forebrain', 'midbrain', 'hindbrain', 'spinalcord',
'Proliferation', 'Negative regulators of Cell Cycle',
'progenitors', 'neurons', 'glia',
'Foxg1', 'En2', 'Phox2b', 'Hoxc9', 'Sox3'
]
color_map = {}
i = 4
for c in marker_labels_sep:
if 'brain' in c or 'spinal' in c:
if 'spinal' in c:
color_map[c] = sc_colour
else:
color_map[c] = get_tissue_colour(c.lower())
else:
color_map[c] = sci_colour[i]
i += 1
vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
gene_markers_sep, output_dir=fig_dir, plt_bg=False,vae_data=vae_data, fig_type="svg",
title=f'Markers significant {experiment_name}',
show_plt=False, save_fig=False, color_map=color_map, angle_plot=15)
col_id = len(wt_cols)
ko_cols_i = [i for i, c in enumerate(df_input.columns) if 'ko' in c and 'merged' not in c]
plot_gene_heatmap(df_input, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
df_input.columns, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap, input_col_ids=ko_cols_i,
method='decoding', title=f'RNA KO DEC {experiment_name}')
col_id = 0
wt_cols_i = [i for i, c in enumerate(df_input.columns) if 'wt' in c and 'merged' not in c]
plot_gene_heatmap(df_input, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
df_input.columns, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap, input_col_ids=wt_cols_i,
method='decoding', title=f'RNA WT DEC {experiment_name}')
log2_cols = [c for c in df_input.columns if 'log2Fold' in c]
log2_cols_i = [i for i, c in enumerate(df_input.columns) if 'log2Fold' in c]
plot_gene_heatmap(df_input, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
df_input.columns, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=div_cmap, input_col_ids=log2_cols_i,
method='decoding', title=f'log2FC DEC {experiment_name}')
h3k_cols = [c for c in df_input.columns if 'H3K27me3' in c and 'signal' in c and 'median' not in c and 'brain' in c]
h3k_cols_i = [i for i, c in enumerate(df_input.columns) if 'H3K27me3' in c and 'signal' in c and 'median' not in c and 'brain' in c]
plot_gene_heatmap(df_input, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
df_input.columns, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=hist_cmap, input_col_ids=h3k_cols_i,
method='decoding', title=f'H3K WT DEC {experiment_name}')
# Ploot all the raw H3K values on teh scatter plot
h3k_cols = [c for c in df.columns if 'H3K27me3' in c and 'signal' in c and 'median' not in c and 'brain' in c]
tmp_df = df.fillna(0)
feature_obs_ax = vae_vis.plot_feature_scatters(tmp_df, "", columns=h3k_cols, vae_data=vae_data,
show_plt=False, output_dir=fig_dir, fig_type="svg",
save_fig=False, title=f'{experiment_name}',
angle_plot=315, cmap=hist_cmap, vmin=0, vmax=10)
# Plot genes on heatmap
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
ko_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=True, cmap=rna_cmap,
method='input', title=f'RNA KO {experiment_name}')
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
wt_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=True, cmap=rna_cmap,
method='input', title=f'RNA WT {experiment_name}')
h3k_cols = [c for c in df.columns if 'H3K27me3' in c and 'signal' in c and 'median' not in c and 'brain' in c]
print(h3k_cols)
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
h3k_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=True, cmap=hist_cmap,
method='input', title=f'H3K WT {experiment_name}')
log2_cols = [c for c in df.columns if 'log2Fold' in c]
print(log2_cols)
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
log2_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=True, cmap=div_cmap,
method='input', title=f'log2FC {experiment_name}')
ko_cols = [c for c in df_all if 'ko' in c and 'merged' in c]
plot_gene_heatmap(df_all, ['Sox1'], ['Sox2'], ['Sox3'], [],
ko_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=True, cmap=rna_cmap,
method='input', title=f'Sox RNA KO {experiment_name}')
wt_cols = [c for c in df_all if 'wt' in c and 'merged' in c]
plot_gene_heatmap(df_all, ['Sox1'], ['Sox2'], ['Sox3'], [],
wt_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=True, cmap=rna_cmap,
method='input', title=f'Sox RNA WT {experiment_name}')
plot_gene_heatmap(df, ['Sox3'], ['Foxg1'], ['En2'], ['Hoxc9'],
['VAE0', 'VAE1', 'VAE2'], all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=bin_cmap,
method='input', title=f'Latent {experiment_name}')
# Plot feature correlation
vae_vis.plot_feature_correlation(df_observed, '', columns=observed_cols, show_plt=True, cmap="RdBu_r",
title=f'Feature vs observed features {experiment_name}',
output_dir=fig_dir, save_fig=True)
# Plot feature correlations for heatmap
vae_vis.plot_feature_correlation(df, '', columns=vae_cols, show_plt=True, cmap='RdBu_r',
output_dir=fig_dir, save_fig=True, title=f'features VAE {experiment_name}')
"""
Plot the heatmaps of the Hox and other tisue specific genes
"""
plot_gene_heatmap(df_all, ['Pax2'], ['Hoxb9'], ['Hoxc9'], ['Hoxd9'],
wt_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
method='input', title=f'Sox pax2 RNA WT {experiment_name}')
plot_gene_heatmap(df_all, ['Pax2'], ['Hoxb9'], ['Hoxc9'], ['Hoxd9'],
ko_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
method='input', title=f'pax2 RNA KO {experiment_name}')
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Phox2b', 'Krox20', 'Fev', 'Hoxb1', 'Hoxd3']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4', 'Pax7', 'Lhx9', 'Evx1', 'Sox14',
'Gata3', 'Dmbx1', 'Tal1', 'Pou4f3', 'Pouf4f2', 'Gata3', 'Barhl1', 'Pax5', 'Dmbx1',
'Pou4f1', 'Irx4', 'Pax7', 'Pax1', 'Pax8', 'Ebf2', 'Patx3', 'Onecut1', 'Otx2',
'Lhx5', 'Irx3', 'Irx5', 'Ebf3', 'Foxb1', 'Irx2', 'Shox2', 'Foxa2', 'Irx1', 'Tcfap2b']
plot_gene_heatmap(df_all, ['Otx2'], ['En2'], ['Phox2b'], ['Hoxd3'],
wt_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
method='input', title=f'Mid RNA WT {experiment_name}')
plot_gene_heatmap(df_all, ['Otx2'], ['En2'], ['Phox2b'], ['Hoxd3'],
ko_cols, all_vae_data, vae_mse,
gene_name, mark='H3K27me3', merge_reps=False, cmap=rna_cmap,
method='input', title=f'Mid RNA KO {experiment_name}')
# Plot gene names on scatter
gene_markers_sep = [['Cdkn1a'], ['Cdkn2a'], ['Cdkn2b'], ['Cip2a'], ['Cdc25c'], ['Mcm10'],
['E2f1'], ['Ccna2'], ['Ccnd1'], ['Aqp4'], ['Snap25'], ['Hes5']
]
marker_labels_sep = ['Cdkn1a', 'Cdkn2a', 'Cdkn2b', 'Cip2a',
'Cdc25c', 'Mcm10',
'E2f1', 'Ccna2', 'Ccnd1',
'Aqp4', 'Snap25', 'Hes5'
]
color_map = {}
i = 4
for c in marker_labels_sep:
if 'brain' in c or 'spinal' in c:
if 'spinal' in c:
color_map[c] = sc_colour
else:
color_map[c] = get_tissue_colour(c.lower())
else:
color_map[c] = sci_colour[i]
i += 1
vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
gene_markers_sep, output_dir=fig_dir, plt_bg=False,vae_data=vae_data, fig_type="svg",
title=f'Markers specific {experiment_name}',
show_plt=False, save_fig=False, color_map=color_map, angle_plot=315)
external_gene_name wt11fb_merged-rep wt13fb_merged-rep wt15fb_merged-rep \ 0 Foxg1 0.876270 0.971738 1.000000 1 Lhx6 0.523575 0.918995 0.942159 2 Tbr1 0.699981 0.964443 1.000000 3 Emx1 0.835460 1.000000 0.930334 4 Eomes 0.846631 1.000000 0.988013 wt18fb_merged-rep wt11mb_merged-rep wt13mb_merged-rep wt15mb_merged-rep \ 0 0.986938 0.373690 0.002306 0.021302 1 1.000000 0.307570 0.193575 0.177374 2 0.988598 0.398743 0.167463 0.246851 3 0.803598 0.396227 0.223458 0.136889 4 0.851878 0.517927 0.330729 0.369411 wt18mb_merged-rep wt11hb_merged-rep wt13hb_merged-rep wt15hb_merged-rep \ 0 0.155632 0.292829 0.074193 0.048903 1 0.327673 0.309588 0.000000 0.053135 2 0.276605 0.007015 0.015224 0.022867 3 0.070040 0.036411 0.001018 0.002749 4 0.565673 0.038614 0.049856 0.060991 wt18hb_merged-rep wt11sc_merged-rep wt13sc_merged-rep wt15sc_merged-rep \ 0 0.043263 0.050391 0.007295 0.000000 1 0.173792 0.260517 0.233555 0.259264 2 0.065809 0.000000 0.015840 0.046058 3 0.005990 0.029781 0.007285 0.000000 4 0.387983 0.002949 0.019690 0.027024 wt18sc_merged-rep 0 0.010509 1 0.239460 2 0.020940 3 0.003238 4 0.000000
external_gene_name ko11fb_merged-rep ko13fb_merged-rep ko15fb_merged-rep \ 0 Foxg1 0.773348 1.000000 0.887473 1 Lhx6 0.398225 0.687109 0.758063 2 Tbr1 0.403798 1.000000 0.914116 3 Emx1 0.741702 1.000000 0.729911 4 Eomes 0.672796 1.000000 0.738654 ko18fb_merged-rep ko11mb_merged-rep ko13mb_merged-rep ko15mb_merged-rep \ 0 0.849753 0.331556 0.553470 0.618689 1 1.000000 0.179405 0.133045 0.535425 2 0.857413 0.031918 0.452307 0.592030 3 0.616446 0.185984 0.270611 0.277571 4 0.527092 0.124368 0.442612 0.448207 ko18mb_merged-rep ko11hb_merged-rep ko13hb_merged-rep ko15hb_merged-rep \ 0 0.631299 0.738634 0.558173 0.574097 1 0.910984 0.461606 0.381082 0.704271 2 0.580405 0.415415 0.470464 0.569589 3 0.247316 0.656964 0.096604 0.200821 4 0.264649 0.663738 0.160908 0.311206 ko18hb_merged-rep ko11sc_merged-rep ko13sc_merged-rep ko15sc_merged-rep \ 0 0.570762 0.0 0.334478 0.403897 1 0.941213 0.0 0.234253 0.533167 2 0.549105 0.0 0.343418 0.473450 3 0.225002 0.0 0.036088 0.176869 4 0.038210 0.0 0.142803 0.128960 ko18sc_merged-rep 0 0.419253 1 0.639234 2 0.475328 3 0.149203 4 0.000499
external_gene_name wt11fb1 wt11fb2 wt13fb1 wt13fb2 wt15fb1 \
0 Foxg1 0.332243 0.361135 0.242759 0.240285 0.086323
1 Lhx6 0.078576 0.089712 0.067405 0.069071 0.000598
2 Tbr1 0.241487 0.263206 0.178108 0.174239 0.053970
3 Emx1 0.150191 0.182136 0.100063 0.096224 0.034206
4 Eomes 0.261289 0.296807 0.163673 0.159432 0.054015
wt15fb2 wt18fb1 wt18fb2 wt11mb1 ... wt18hb1 wt18hb2 wt11sc1 \
0 0.085332 0.046931 0.063953 0.304176 ... 0.043250 0.035738 0.290357
1 0.000000 0.006122 0.002098 0.077322 ... 0.053756 0.040947 0.124480
2 0.050705 0.036403 0.042562 0.211572 ... 0.049917 0.041552 0.213249
3 0.044401 0.026916 0.028873 0.121938 ... 0.041410 0.034799 0.125310
4 0.061966 0.035586 0.037288 0.226348 ... 0.036296 0.024256 0.209931
wt11sc2 wt13sc1 wt13sc2 wt15sc1 wt15sc2 wt18sc1 wt18sc2
0 0.308961 0.127086 0.137268 0.167169 0.079765 0.021216 0.034060
1 0.134081 0.080590 0.102226 0.103712 0.064989 0.044420 0.046667
2 0.229920 0.099434 0.115627 0.137251 0.066290 0.031970 0.036905
3 0.135449 0.034873 0.043890 0.090604 0.036620 0.027829 0.025904
4 0.227468 0.060794 0.069618 0.114196 0.044368 0.015146 0.022080
[5 rows x 33 columns]
external_gene_name VAE0 VAE1 VAE2 0 Foxg1 -1.992226 -1.512505 -0.965825 1 Lhx6 -1.610574 -1.708264 -0.150862 2 Tbr1 -1.700867 -1.497262 -0.558968 3 Emx1 -2.198543 -0.923434 0.100433 4 Eomes -2.382952 -1.224252 -0.452121
external_gene_name wt11fb_merged-rep wt13fb_merged-rep wt15fb_merged-rep \ 0 Hes1 1.000000 0.859668 0.469616 1 Sox2 0.943380 0.949568 0.707070 2 Hes5 0.651925 0.732021 0.321306 3 Sox1 0.911189 1.000000 0.836312 4 Sox3 0.950178 0.927460 0.611877 wt18fb_merged-rep wt11mb_merged-rep wt13mb_merged-rep wt15mb_merged-rep \ 0 0.086373 0.652471 0.275798 0.029628 1 0.550415 1.000000 0.622615 0.217100 2 0.193179 1.000000 0.900866 0.299143 3 0.786355 0.932404 0.751252 0.426557 4 0.358576 1.000000 0.755177 0.356305 wt18mb_merged-rep wt11hb_merged-rep wt13hb_merged-rep wt15hb_merged-rep \ 0 0.000000 0.594827 0.201936 0.023484 1 0.375949 0.853657 0.468725 0.236694 2 0.280754 0.843860 0.450375 0.273479 3 0.474636 0.800309 0.481115 0.281785 4 0.370077 0.935815 0.379595 0.191047 wt18hb_merged-rep wt11sc_merged-rep wt13sc_merged-rep wt15sc_merged-rep \ 0 0.012934 0.759841 0.314388 0.321688 1 0.221981 0.794292 0.420991 0.000000 2 0.301305 0.623341 0.463581 0.000000 3 0.232632 0.417540 0.215308 0.000000 4 0.258589 0.774401 0.244539 0.000000 wt18sc_merged-rep 0 0.007980 1 0.230233 2 0.210562 3 0.126464 4 0.150160
external_gene_name ko11fb_merged-rep ko13fb_merged-rep ko15fb_merged-rep \ 0 Hes1 1.000000 0.855647 0.303016 1 Sox2 1.000000 0.629236 0.294420 2 Hes5 0.980069 0.589436 0.097420 3 Sox1 1.000000 0.716547 0.494781 4 Sox3 1.000000 0.511926 0.243986 ko18fb_merged-rep ko11mb_merged-rep ko13mb_merged-rep ko15mb_merged-rep \ 0 0.206161 0.596411 0.693812 0.215795 1 0.292689 0.832062 0.463111 0.152282 2 0.157003 1.000000 0.453238 0.000000 3 0.460824 0.723482 0.314919 0.235458 4 0.136114 0.924000 0.277327 0.051959 ko18mb_merged-rep ko11hb_merged-rep ko13hb_merged-rep ko15hb_merged-rep \ 0 0.253627 0.820008 0.340260 0.159875 1 0.188908 0.937412 0.129238 0.000000 2 0.224047 0.972094 0.104580 0.046064 3 0.241585 0.899023 0.001515 0.077447 4 0.137127 0.996363 0.046774 0.000000 ko18hb_merged-rep ko11sc_merged-rep ko13sc_merged-rep ko15sc_merged-rep \ 0 0.327860 0.820154 0.243983 0.000000 1 0.066738 0.787053 0.182959 0.011233 2 0.205237 0.830015 0.311043 0.132207 3 0.130070 0.202840 0.042317 0.000000 4 0.024383 0.852967 0.169373 0.088402 ko18sc_merged-rep 0 0.234035 1 0.162649 2 0.199092 3 0.113725 4 0.141788
external_gene_name wt11fb1 wt11fb2 wt13fb1 wt13fb2 wt15fb1 \
0 Hes1 0.470657 0.496916 0.415665 0.410116 0.260262
1 Sox2 1.000000 0.976060 0.985133 0.965742 0.907833
2 Hes5 0.845298 0.854981 0.813386 0.794306 0.683071
3 Sox1 0.651308 0.665131 0.577448 0.569460 0.412998
4 Sox3 0.982694 0.976316 0.887595 0.879618 0.717153
wt15fb2 wt18fb1 wt18fb2 wt11mb1 ... wt18hb1 wt18hb2 wt11sc1 \
0 0.244496 0.217397 0.248247 0.442810 ... 0.210489 0.214836 0.429885
1 0.859424 0.848084 0.891070 0.971654 ... 0.817609 0.836690 0.939905
2 0.626108 0.606155 0.653275 0.812678 ... 0.580938 0.603006 0.775985
3 0.388546 0.340035 0.385566 0.625158 ... 0.318352 0.336254 0.609425
4 0.693138 0.567790 0.637941 0.950156 ... 0.505093 0.538485 0.945185
wt11sc2 wt13sc1 wt13sc2 wt15sc1 wt15sc2 wt18sc1 wt18sc2
0 0.442498 0.308816 0.323675 0.326800 0.251081 0.194568 0.208579
1 0.937574 0.913454 0.910703 0.877303 0.828270 0.799279 0.826112
2 0.783537 0.691768 0.687246 0.695495 0.618541 0.569273 0.582989
3 0.620817 0.478901 0.486213 0.461890 0.392189 0.307503 0.328466
4 0.946852 0.676686 0.664558 0.712151 0.604417 0.506605 0.521694
[5 rows x 33 columns]
external_gene_name VAE0 VAE1 VAE2 0 Hes1 -1.563227 -1.595908 -1.151025 1 Sox2 -0.413877 -1.141836 -1.758093 2 Hes5 -1.133250 -1.227582 -1.471454 3 Sox1 -1.626122 -1.897884 -1.742238 4 Sox3 -1.340308 -0.134940 -1.315866
<Figure size 360x360 with 0 Axes>
external_gene_name ko11fb1 ko11fb2 ko13fb1 ko13fb2 ko15fb1 \
0 Sox3 0.550487 0.580333 0.440429 0.437239 0.366737
1 Foxg1 0.384186 0.407571 0.325194 0.321578 0.274053
2 En2 0.337478 0.309805 0.365119 0.354549 0.322877
3 Hoxc9 0.145622 0.076693 0.328610 0.321775 0.306653
ko15fb2 ko18fb1 ko18fb2 ko11mb1 ... ko18hb1 ko18hb2 ko11sc1 \
0 0.364015 0.333960 0.341242 0.532717 ... 0.305389 0.312127 0.539109
1 0.267562 0.263634 0.275351 0.389638 ... 0.253678 0.256894 0.372105
2 0.316797 0.322534 0.323716 0.370418 ... 0.322744 0.327505 0.377411
3 0.290520 0.297720 0.302993 0.225737 ... 0.331068 0.358566 0.289961
ko11sc2 ko13sc1 ko13sc2 ko15sc1 ko15sc2 ko18sc1 ko18sc2
0 0.544851 0.369141 0.379123 0.313824 0.337723 0.305011 0.323990
1 0.369299 0.284971 0.286167 0.245121 0.242463 0.235434 0.245754
2 0.353475 0.373311 0.373445 0.323736 0.314951 0.313176 0.325474
3 0.238206 0.412175 0.400515 0.361697 0.347344 0.337675 0.369979
[4 rows x 33 columns]
<Figure size 144x144 with 0 Axes>
external_gene_name wt11fb1 wt11fb2 wt13fb1 wt13fb2 wt15fb1 \
0 Sox3 0.570532 0.566920 0.516674 0.512157 0.420147
1 Foxg1 0.396663 0.415054 0.339704 0.338129 0.240128
2 En2 0.299162 0.302716 0.297276 0.293493 0.241776
3 Hoxc9 0.068199 0.063579 0.100579 0.111775 0.115555
wt15fb2 wt18fb1 wt18fb2 wt11mb1 ... wt18hb1 wt18hb2 wt11sc1 \
0 0.406546 0.335557 0.375286 0.552104 ... 0.300049 0.318960 0.549289
1 0.239497 0.215054 0.225889 0.378798 ... 0.212711 0.207929 0.370002
2 0.235451 0.239219 0.240315 0.310785 ... 0.279884 0.277704 0.351801
3 0.118861 0.130621 0.127169 0.100477 ... 0.257541 0.269845 0.229966
wt11sc2 wt13sc1 wt13sc2 wt15sc1 wt15sc2 wt18sc1 wt18sc2
0 0.550234 0.397229 0.390360 0.417314 0.356300 0.300906 0.309451
1 0.381843 0.266075 0.272556 0.291589 0.235953 0.198685 0.206861
2 0.354072 0.337851 0.355825 0.321393 0.302242 0.279390 0.284407
3 0.216939 0.323666 0.355716 0.284004 0.302868 0.300545 0.306760
[4 rows x 33 columns]
external_gene_name log2FoldChange_fb log2FoldChange_mb log2FoldChange_hb \ 0 Sox3 0.206852 0.229349 0.193985 1 Foxg1 0.316669 0.291584 0.394493 2 En2 0.441420 0.380644 0.428319 3 Hoxc9 0.899163 0.874974 0.905235 log2FoldChange_sc log2FoldChange_a11 log2FoldChange_a13 \ 0 0.212825 0.206744 0.173499 1 0.380661 0.259872 0.248273 2 0.441557 0.411308 0.368928 3 0.882775 0.876294 0.866535 log2FoldChange_a15 log2FoldChange_a18 log2FoldChange_p11 \ 0 0.179354 0.213204 0.183859 1 0.276262 0.336427 0.241426 2 0.379380 0.416984 0.343115 3 0.874709 0.903284 0.824419 log2FoldChange_p13 log2FoldChange_p15 log2FoldChange_p18 0 0.252860 0.180744 0.186477 1 0.413167 0.348606 0.388397 2 0.455097 0.412187 0.410272 3 0.873342 0.876871 0.945279
external_gene_name \ 0 Sox3 1 Foxg1 2 En2 3 Hoxc9 forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal_log2 \ 0 0.128510 1 0.694275 2 0.830069 3 1.000000 forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal_log2 \ 0 0.013997 1 0.642868 2 0.806986 3 1.000000 forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal_log2 \ 0 0.252667 1 0.694428 2 0.807150 3 1.000000 forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal_log2 \ 0 0.216872 1 0.737926 2 0.825766 3 1.000000 forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal_log2 \ 0 0.019455 1 0.665228 2 0.778123 3 1.000000 forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal_log2 \ 0 0.077944 1 0.683991 2 0.758337 3 1.000000 forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal_log2 \ 0 0.042131 1 0.625534 2 0.695705 3 1.000000 hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal_log2 \ 0 0.088626 1 0.699155 2 0.829679 3 1.000000 hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal_log2 \ 0 0.020220 1 0.686352 2 0.803702 3 1.000000 ... \ 0 ... 1 ... 2 ... 3 ... hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal_log2 \ 0 0.200723 1 0.821710 2 0.839977 3 1.000000 hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal_log2 \ 0 0.301032 1 0.803528 2 0.802012 3 0.997743 hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal_log2 \ 0 0.019209 1 0.715712 2 0.792974 3 1.000000 midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal_log2 \ 0 0.150847 1 0.714856 2 0.839047 3 1.000000 midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal_log2 \ 0 0.018569 1 0.669096 2 0.812188 3 1.000000 midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal_log2 \ 0 0.322843 1 0.700308 2 0.793829 3 1.000000 midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal_log2 \ 0 0.390880 1 0.747783 2 0.812979 3 1.000000 midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal_log2 \ 0 0.191405 1 0.816992 2 0.834253 3 1.000000 midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal_log2 \ 0 0.318443 1 0.779879 2 0.804545 3 0.992082 midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal_log2 0 0.117959 1 0.755980 2 0.772538 3 1.000000 [4 rows x 22 columns]
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. self.fig.tight_layout(**tight_params)
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/scivae/vis.py:351: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = plt.figure()
external_gene_name ko11fb1 ko11fb2 ko13fb1 ko13fb2 ko15fb1 \
0 Sox3 0.938785 1.0 0.490566 0.515019 0.282135
1 Foxg1 0.399059 1.0 0.864240 0.907562 0.796720
2 En2 0.999684 0.0 0.568539 0.578491 0.459351
3 Hoxc9 0.332753 0.0 0.942823 0.956517 0.786817
ko15fb2 ko18fb1 ko18fb2 ko11mb1 ... ko18hb1 ko18hb2 ko11sc1 \
0 0.211148 0.128313 0.158717 0.803567 ... 0.004166 0.069234 0.781372
1 0.790024 0.760058 0.764653 0.424918 ... 0.533096 0.532798 0.127241
2 0.476414 0.399936 0.469709 0.815591 ... 0.479072 0.534291 0.410039
3 0.795932 0.650076 0.710400 0.552940 ... 0.589719 0.624295 1.000000
ko11sc2 ko13sc1 ko13sc2 ko15sc1 ko15sc2 ko18sc1 ko18sc2
0 0.876284 0.152762 0.197861 0.077266 0.118539 0.139331 0.158548
1 0.000000 0.369131 0.308179 0.428317 0.363157 0.430157 0.386570
2 0.081347 0.564060 0.568928 0.516123 0.428650 0.405513 0.415569
3 0.968066 0.899169 0.913084 0.757540 0.819714 0.760162 0.752785
[4 rows x 33 columns]
<Figure size 360x360 with 0 Axes>
external_gene_name wt11fb1 wt11fb2 wt13fb1 wt13fb2 wt15fb1 \
0 Sox3 0.987655 0.888969 0.919341 0.914714 0.548995
1 Foxg1 0.905497 0.839103 0.962206 0.971014 0.989059
2 En2 0.021750 0.076919 0.006142 0.011685 0.030719
3 Hoxc9 0.016999 0.005155 0.000000 0.006330 0.000000
wt15fb2 wt18fb1 wt18fb2 wt11mb1 ... wt18hb1 wt18hb2 wt11sc1 \
0 0.693739 0.294510 0.473605 1.000000 ... 0.320858 0.259908 0.760646
1 1.000000 0.972821 0.990432 0.366511 ... 0.057609 0.041185 0.055598
2 0.032995 0.030337 0.048266 0.929953 ... 0.106980 0.112892 0.044118
3 0.035905 0.021010 0.013906 0.012152 ... 0.010465 0.473454 0.926013
wt11sc2 wt13sc1 wt13sc2 wt15sc1 wt15sc2 wt18sc1 wt18sc2
0 0.786617 0.276225 0.278214 0.000000 0.096237 0.216120 0.161478
1 0.057279 0.011313 0.016418 0.013319 0.000000 0.006830 0.027252
2 0.067930 0.009606 0.027778 0.011913 0.015164 0.019509 0.014426
3 0.919263 1.000000 0.995818 0.784096 0.892346 0.852279 0.898904
[4 rows x 33 columns]
['forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal', 'forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal', 'forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal', 'forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal', 'forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal', 'forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal', 'forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal', 'hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal', 'hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal', 'hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal', 'hindbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF324KBG_signal', 'hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal', 'hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal', 'hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal', 'midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal', 'midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal', 'midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal', 'midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal', 'midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal', 'midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal', 'midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal'] external_gene_name \ 0 Sox3 1 Foxg1 2 En2 3 Hoxc9 forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal \ 0 1.000000 1 0.983556 2 1.000000 3 1.000000 forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal \ 0 0.000000 1 0.286343 2 0.100743 3 0.283206 forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal \ 0 0.555604 1 0.308459 2 0.249711 3 0.428984 forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal \ 0 0.000000 1 0.000000 2 0.434323 3 0.244270 forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal \ 0 0.000000 1 0.309227 2 0.158969 3 0.231445 forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal \ 0 0.000000 1 0.000000 2 0.049647 3 0.107695 forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal \ 0 0.000000 1 0.000000 2 0.125367 3 0.000000 hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal \ 0 0.654686 1 0.983348 2 0.558443 3 0.897566 hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal ... \ 0 0.000000 ... 1 0.538108 ... 2 0.135460 ... 3 0.272632 ... hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal \ 0 0.541669 1 0.852510 2 0.168170 3 0.260769 hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal \ 0 0.000000 1 0.597450 2 0.052538 3 0.168848 hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal \ 0 0.000000 1 0.483976 2 0.011061 3 0.134280 midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal \ 0 0.660801 1 1.000000 2 0.534473 3 0.610359 midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal \ 0 0.000000 1 0.865415 2 0.049138 3 0.369078 midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal \ 0 0.000000 1 0.966515 2 0.270990 3 0.524347 midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal \ 0 0.710406 1 0.795924 2 0.225399 3 0.206282 midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal \ 0 0.000000 1 0.607118 2 0.117276 3 0.257075 midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal \ 0 0.000000 1 0.414332 2 0.016693 3 0.112401 midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal 0 0.000000 1 0.537933 2 0.000000 3 0.073440 [4 rows x 22 columns]
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/matrix.py:1205: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. self.fig.tight_layout(**tight_params)
['log2FoldChange_fb', 'log2FoldChange_mb', 'log2FoldChange_hb', 'log2FoldChange_sc', 'log2FoldChange_a11', 'log2FoldChange_a13', 'log2FoldChange_a15', 'log2FoldChange_a18', 'log2FoldChange_p11', 'log2FoldChange_p13', 'log2FoldChange_p15', 'log2FoldChange_p18'] external_gene_name log2FoldChange_fb log2FoldChange_mb log2FoldChange_hb \ 0 Sox3 0.186678 0.215256 0.403930 1 Foxg1 0.000000 0.969118 0.883091 2 En2 0.875505 0.000000 0.751336 3 Hoxc9 0.774984 0.905491 0.674293 log2FoldChange_sc log2FoldChange_a11 log2FoldChange_a13 \ 0 0.965154 0.839535 0.000000 1 1.000000 0.211348 0.616524 2 0.938987 0.510267 0.531121 3 0.000000 0.563688 1.000000 log2FoldChange_a15 log2FoldChange_a18 log2FoldChange_p11 \ 0 0.210824 0.444475 1.000000 1 0.515227 0.310291 0.603990 2 0.413526 0.393134 0.291812 3 0.765863 0.740117 0.298132 log2FoldChange_p13 log2FoldChange_p15 log2FoldChange_p18 0 0.544195 0.863352 0.689699 1 0.883013 0.979874 0.953042 2 0.795116 1.000000 0.741950 3 0.445875 0.373453 0.100662
external_gene_name ko11fb_merged-rep ko13fb_merged-rep ko15fb_merged-rep \ 0 Sox1 1.0 0.716547 0.494781 1 Sox2 1.0 0.629236 0.294420 2 Sox3 1.0 0.511926 0.243986 ko18fb_merged-rep ko11mb_merged-rep ko13mb_merged-rep ko15mb_merged-rep \ 0 0.460824 0.723482 0.314919 0.235458 1 0.292689 0.832062 0.463111 0.152282 2 0.136114 0.924000 0.277327 0.051959 ko18mb_merged-rep ko11hb_merged-rep ko13hb_merged-rep ko15hb_merged-rep \ 0 0.241585 0.899023 0.001515 0.077447 1 0.188908 0.937412 0.129238 0.000000 2 0.137127 0.996363 0.046774 0.000000 ko18hb_merged-rep ko11sc_merged-rep ko13sc_merged-rep ko15sc_merged-rep \ 0 0.130070 0.202840 0.042317 0.000000 1 0.066738 0.787053 0.182959 0.011233 2 0.024383 0.852967 0.169373 0.088402 ko18sc_merged-rep 0 0.113725 1 0.162649 2 0.141788
external_gene_name wt11fb_merged-rep wt13fb_merged-rep wt15fb_merged-rep \ 0 Sox1 0.911189 1.000000 0.836312 1 Sox2 0.943380 0.949568 0.707070 2 Sox3 0.950178 0.927460 0.611877 wt18fb_merged-rep wt11mb_merged-rep wt13mb_merged-rep wt15mb_merged-rep \ 0 0.786355 0.932404 0.751252 0.426557 1 0.550415 1.000000 0.622615 0.217100 2 0.358576 1.000000 0.755177 0.356305 wt18mb_merged-rep wt11hb_merged-rep wt13hb_merged-rep wt15hb_merged-rep \ 0 0.474636 0.800309 0.481115 0.281785 1 0.375949 0.853657 0.468725 0.236694 2 0.370077 0.935815 0.379595 0.191047 wt18hb_merged-rep wt11sc_merged-rep wt13sc_merged-rep wt15sc_merged-rep \ 0 0.232632 0.417540 0.215308 0.0 1 0.221981 0.794292 0.420991 0.0 2 0.258589 0.774401 0.244539 0.0 wt18sc_merged-rep 0 0.126464 1 0.230233 2 0.150160
external_gene_name VAE0 VAE1 VAE2 0 Sox3 -1.340308 -0.134940 -1.315866 1 Foxg1 -1.992226 -1.512505 -0.965825 2 En2 -1.350005 -2.014320 -0.434887 3 Hoxc9 -1.353282 -3.168541 0.372992
H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K27me3 0.03880145459733418 1.1304167715365197e-05 H3K4me1 Log2FC FB -0.14880590787152698 2.8859404622978633e-64 H3K4me1 Log2FC MB -0.10387373575786396 4.872008964191265e-32 H3K4me1 Log2FC HB -0.05777615219411477 6.12378414825415e-11 H3K4me1 Log2FC SC -0.07950151137451965 2.1197279496874784e-19 H3K4me1 VAE0 -0.0446884001956768 4.251104865461492e-07 H3K4me1 VAE1 -0.08406049569917753 1.6476006172029343e-21 H3K4me1 VAE2 -0.19398340219121096 9.81715077576278e-109 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K27me3 0.1836859102996935 1.6625204625607636e-97 H3K4me2 Log2FC FB -0.12969526458358954 3.9896551047753745e-49 H3K4me2 Log2FC MB -0.09642120978180409 8.108800676805556e-28 H3K4me2 Log2FC HB -0.10550208768149451 5.2879602412044856e-33 H3K4me2 Log2FC SC -0.08178812465927765 1.9179743858493656e-20 H3K4me2 VAE0 0.08834956848013696 1.335708453202751e-23 H3K4me2 VAE1 -0.2636875996916555 1.5735477679806943e-202 H3K4me2 VAE2 -0.18498827221190547 6.866667382156619e-99 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K27me3 -0.28958561139696254 1.0906807671975684e-245 H3K4me3 Log2FC FB -0.4470506488127457 0.0 H3K4me3 Log2FC MB -0.4473702882601094 0.0 H3K4me3 Log2FC HB -0.41979679249095553 0.0 H3K4me3 Log2FC SC -0.3297606127199917 0.0 H3K4me3 VAE0 0.30597343061378124 1.8027240014084392e-275 H3K4me3 VAE1 0.07296409905287944 1.4064868617096597e-16 H3K4me3 VAE2 -0.5298862221619517 0.0 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K27me3 -0.2526689336918533 1.4216280599037781e-185 H3K27ac Log2FC FB -0.3815669720025924 0.0 H3K27ac Log2FC MB -0.42208837018425827 0.0 H3K27ac Log2FC HB -0.408298389874337 0.0 H3K27ac Log2FC SC -0.2977765740809075 2.491982785246384e-260 H3K27ac VAE0 0.3514588028990406 0.0 H3K27ac VAE1 0.05627016617662167 1.8912472752821038e-10 H3K27ac VAE2 -0.4482816481694506 0.0 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K27me3 -0.35415360112947697 0.0 H3K36me3 Log2FC FB -0.4744138412763159 0.0 H3K36me3 Log2FC MB -0.430059873023584 0.0 H3K36me3 Log2FC HB -0.3634108408611482 0.0 H3K36me3 Log2FC SC -0.33209079328572233 0.0 H3K36me3 VAE0 0.20757601112819188 1.4444036902704797e-124 H3K36me3 VAE1 0.18686624881923297 6.647406888366817e-101 H3K36me3 VAE2 -0.5117903743601302 0.0 H3K27me3 H3K4me1 0.03880145459733418 1.1304167715365197e-05 H3K27me3 H3K4me2 0.18368591029969353 1.662520462560716e-97 H3K27me3 H3K4me3 -0.2895856113969625 1.0906807671978166e-245 H3K27me3 H3K27ac -0.2526689336918533 1.4216280599037781e-185 H3K27me3 H3K36me3 -0.35415360112947697 0.0 H3K27me3 H3K27me3 1.0 0.0 H3K27me3 Log2FC FB 0.37643342244583206 0.0 H3K27me3 Log2FC MB 0.3225460106810916 1.314030964712288e-307 H3K27me3 Log2FC HB 0.2347875537574898 8.590917791342444e-160 H3K27me3 Log2FC SC 0.2956826706138225 1.5329233866307224e-256 H3K27me3 VAE0 -0.10520428908479008 7.957836150465658e-33 H3K27me3 VAE1 -0.8608600491616873 0.0 H3K27me3 VAE2 0.2279197055845889 1.8859458409143461e-150 Log2FC FB H3K4me1 -0.14880590787152698 2.8859404622978633e-64 Log2FC FB H3K4me2 -0.12969526458358954 3.9896551047753745e-49 Log2FC FB H3K4me3 -0.4470506488127457 0.0 Log2FC FB H3K27ac -0.3815669720025924 0.0 Log2FC FB H3K36me3 -0.4744138412763159 0.0 Log2FC FB H3K27me3 0.37643342244583206 0.0 Log2FC FB Log2FC FB 1.0 0.0 Log2FC FB Log2FC MB 0.7040491449224625 0.0 Log2FC FB Log2FC HB 0.5367783316680511 0.0 Log2FC FB Log2FC SC 0.5730464144717174 0.0 Log2FC FB VAE0 0.0035828141346172554 0.6852839419752368 Log2FC FB VAE1 -0.3026340104905353 3.046326192390388e-269 Log2FC FB VAE2 0.5758155364107505 0.0 Log2FC MB H3K4me1 -0.10387373575786396 4.872008964191265e-32 Log2FC MB H3K4me2 -0.09642120978180409 8.108800676805556e-28 Log2FC MB H3K4me3 -0.44737028826010944 0.0 Log2FC MB H3K27ac -0.4220883701842583 0.0 Log2FC MB H3K36me3 -0.43005987302358406 0.0 Log2FC MB H3K27me3 0.32254601068109157 1.314030964712587e-307 Log2FC MB Log2FC FB 0.7040491449224625 0.0 Log2FC MB Log2FC MB 0.9999999999999999 0.0 Log2FC MB Log2FC HB 0.7578212671301587 0.0 Log2FC MB Log2FC SC 0.5751592030701783 0.0 Log2FC MB VAE0 -0.17513450220728732 1.1229971545114095e-88 Log2FC MB VAE1 -0.21321910758293916 1.757463064410544e-131 Log2FC MB VAE2 0.46938929025701914 0.0 Log2FC HB H3K4me1 -0.05777615219411477 6.12378414825415e-11 Log2FC HB H3K4me2 -0.10550208768149451 5.2879602412044856e-33 Log2FC HB H3K4me3 -0.4197967924909555 0.0 Log2FC HB H3K27ac -0.408298389874337 0.0 Log2FC HB H3K36me3 -0.36341084086114817 0.0 Log2FC HB H3K27me3 0.2347875537574898 8.590917791342444e-160 Log2FC HB Log2FC FB 0.5367783316680511 0.0 Log2FC HB Log2FC MB 0.7578212671301588 0.0 Log2FC HB Log2FC HB 1.0 0.0 Log2FC HB Log2FC SC 0.47172347343211496 0.0 Log2FC HB VAE0 -0.3982180606696101 0.0 Log2FC HB VAE1 -0.07716379044745166 2.3055509300983664e-18 Log2FC HB VAE2 0.2883184296728832 1.8180376139160885e-243 Log2FC SC H3K4me1 -0.07950151137451963 2.1197279496875092e-19 Log2FC SC H3K4me2 -0.08178812465927764 1.9179743858494207e-20 Log2FC SC H3K4me3 -0.3297606127199917 0.0 Log2FC SC H3K27ac -0.2977765740809076 2.4919827852461e-260 Log2FC SC H3K36me3 -0.33209079328572233 0.0 Log2FC SC H3K27me3 0.2956826706138225 1.5329233866307224e-256 Log2FC SC Log2FC FB 0.5730464144717174 0.0 Log2FC SC Log2FC MB 0.5751592030701783 0.0 Log2FC SC Log2FC HB 0.47172347343211496 0.0 Log2FC SC Log2FC SC 0.9999999999999999 0.0 Log2FC SC VAE0 0.04095923910797624 3.570096858074452e-06 Log2FC SC VAE1 -0.24496989653146614 3.2652391791136445e-174 Log2FC SC VAE2 0.4631936937538506 0.0 VAE0 H3K4me1 -0.0446884001956768 4.251104865461492e-07 VAE0 H3K4me2 0.08834956848013696 1.335708453202751e-23 VAE0 H3K4me3 0.30597343061378124 1.8027240014084392e-275 VAE0 H3K27ac 0.3514588028990406 0.0 VAE0 H3K36me3 0.20757601112819188 1.4444036902704797e-124 VAE0 H3K27me3 -0.10520428908479008 7.957836150465658e-33 VAE0 Log2FC FB 0.003582814134617256 0.6852839419752368 VAE0 Log2FC MB -0.17513450220728735 1.1229971545113458e-88 VAE0 Log2FC HB -0.39821806066961 0.0 VAE0 Log2FC SC 0.04095923910797624 3.570096858074452e-06 VAE0 VAE0 1.0 0.0 VAE0 VAE1 -0.17727771491123703 7.574731590825203e-91 VAE0 VAE2 0.024102462909318584 0.006397261512370033 VAE1 H3K4me1 -0.08406049569917753 1.6476006172029343e-21 VAE1 H3K4me2 -0.2636875996916555 1.5735477679806943e-202 VAE1 H3K4me3 0.07296409905287946 1.4064868617096594e-16 VAE1 H3K27ac 0.05627016617662167 1.8912472752821038e-10 VAE1 H3K36me3 0.18686624881923297 6.647406888366817e-101 VAE1 H3K27me3 -0.8608600491616872 0.0 VAE1 Log2FC FB -0.30263401049053534 3.046326192389002e-269 VAE1 Log2FC MB -0.2132191075829392 1.757463064410544e-131 VAE1 Log2FC HB -0.07716379044745164 2.3055509300984496e-18 VAE1 Log2FC SC -0.2449698965314661 3.2652391791142007e-174 VAE1 VAE0 -0.17727771491123703 7.574731590825203e-91 VAE1 VAE1 1.0 0.0 VAE1 VAE2 0.015624110732809882 0.077162199979443 VAE2 H3K4me1 -0.19398340219121096 9.81715077576278e-109 VAE2 H3K4me2 -0.18498827221190545 6.86666738215701e-99 VAE2 H3K4me3 -0.5298862221619517 0.0 VAE2 H3K27ac -0.4482816481694506 0.0 VAE2 H3K36me3 -0.5117903743601302 0.0 VAE2 H3K27me3 0.2279197055845889 1.8859458409143461e-150 VAE2 Log2FC FB 0.5758155364107505 0.0 VAE2 Log2FC MB 0.46938929025701914 0.0 VAE2 Log2FC HB 0.2883184296728831 1.8180376139162956e-243 VAE2 Log2FC SC 0.4631936937538506 0.0 VAE2 VAE0 0.024102462909318584 0.006397261512370033 VAE2 VAE1 0.015624110732809882 0.077162199979443 VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
H3K4me1 H3K4me1 1.0 0.0 H3K4me1 H3K4me2 0.39749347776849686 0.0 H3K4me1 H3K4me3 0.28172402945842107 4.3402046596620704e-232 H3K4me1 H3K27ac 0.2362835918111678 7.214904776823545e-162 H3K4me1 H3K36me3 0.22929575895557416 2.6822018034629003e-152 H3K4me1 H3K27me3 0.03880145459733418 1.1304167715365197e-05 H3K4me1 Log2FC FB -0.14880590787152698 2.8859404622978633e-64 H3K4me1 Log2FC MB -0.10387373575786396 4.872008964191265e-32 H3K4me1 Log2FC HB -0.05777615219411477 6.12378414825415e-11 H3K4me1 Log2FC SC -0.07950151137451965 2.1197279496874784e-19 H3K4me1 VAE0 -0.0446884001956768 4.251104865461492e-07 H3K4me1 VAE1 -0.08406049569917753 1.6476006172029343e-21 H3K4me1 VAE2 -0.19398340219121096 9.81715077576278e-109 H3K4me2 H3K4me1 0.39749347776849686 0.0 H3K4me2 H3K4me2 1.0 0.0 H3K4me2 H3K4me3 0.46233360809517227 0.0 H3K4me2 H3K27ac 0.2931385611078129 5.576842401477536e-252 H3K4me2 H3K36me3 0.16555036365358755 2.6180050261417907e-79 H3K4me2 H3K27me3 0.1836859102996935 1.6625204625607636e-97 H3K4me2 Log2FC FB -0.12969526458358954 3.9896551047753745e-49 H3K4me2 Log2FC MB -0.09642120978180409 8.108800676805556e-28 H3K4me2 Log2FC HB -0.10550208768149451 5.2879602412044856e-33 H3K4me2 Log2FC SC -0.08178812465927765 1.9179743858493656e-20 H3K4me2 VAE0 0.08834956848013696 1.335708453202751e-23 H3K4me2 VAE1 -0.2636875996916555 1.5735477679806943e-202 H3K4me2 VAE2 -0.18498827221190547 6.866667382156619e-99 H3K4me3 H3K4me1 0.281724029458421 4.340204659663059e-232 H3K4me3 H3K4me2 0.46233360809517227 0.0 H3K4me3 H3K4me3 0.9999999999999999 0.0 H3K4me3 H3K27ac 0.7057105100332491 0.0 H3K4me3 H3K36me3 0.5041247938852597 0.0 H3K4me3 H3K27me3 -0.28958561139696254 1.0906807671975684e-245 H3K4me3 Log2FC FB -0.4470506488127457 0.0 H3K4me3 Log2FC MB -0.4473702882601094 0.0 H3K4me3 Log2FC HB -0.41979679249095553 0.0 H3K4me3 Log2FC SC -0.3297606127199917 0.0 H3K4me3 VAE0 0.30597343061378124 1.8027240014084392e-275 H3K4me3 VAE1 0.07296409905287944 1.4064868617096597e-16 H3K4me3 VAE2 -0.5298862221619517 0.0 H3K27ac H3K4me1 0.23628359181116781 7.214904776822724e-162 H3K27ac H3K4me2 0.29313856110781294 5.576842401476902e-252 H3K27ac H3K4me3 0.7057105100332491 0.0 H3K27ac H3K27ac 0.9999999999999999 0.0 H3K27ac H3K36me3 0.43544931623713806 0.0 H3K27ac H3K27me3 -0.2526689336918533 1.4216280599037781e-185 H3K27ac Log2FC FB -0.3815669720025924 0.0 H3K27ac Log2FC MB -0.42208837018425827 0.0 H3K27ac Log2FC HB -0.408298389874337 0.0 H3K27ac Log2FC SC -0.2977765740809075 2.491982785246384e-260 H3K27ac VAE0 0.3514588028990406 0.0 H3K27ac VAE1 0.05627016617662167 1.8912472752821038e-10 H3K27ac VAE2 -0.4482816481694506 0.0 H3K36me3 H3K4me1 0.22929575895557416 2.6822018034629003e-152 H3K36me3 H3K4me2 0.16555036365358758 2.618005026141492e-79 H3K36me3 H3K4me3 0.5041247938852595 0.0 H3K36me3 H3K27ac 0.43544931623713806 0.0 H3K36me3 H3K36me3 1.0 0.0 H3K36me3 H3K27me3 -0.35415360112947697 0.0 H3K36me3 Log2FC FB -0.4744138412763159 0.0 H3K36me3 Log2FC MB -0.430059873023584 0.0 H3K36me3 Log2FC HB -0.3634108408611482 0.0 H3K36me3 Log2FC SC -0.33209079328572233 0.0 H3K36me3 VAE0 0.20757601112819188 1.4444036902704797e-124 H3K36me3 VAE1 0.18686624881923297 6.647406888366817e-101 H3K36me3 VAE2 -0.5117903743601302 0.0 H3K27me3 H3K4me1 0.03880145459733418 1.1304167715365197e-05 H3K27me3 H3K4me2 0.18368591029969353 1.662520462560716e-97 H3K27me3 H3K4me3 -0.2895856113969625 1.0906807671978166e-245 H3K27me3 H3K27ac -0.2526689336918533 1.4216280599037781e-185 H3K27me3 H3K36me3 -0.35415360112947697 0.0 H3K27me3 H3K27me3 1.0 0.0 H3K27me3 Log2FC FB 0.37643342244583206 0.0 H3K27me3 Log2FC MB 0.3225460106810916 1.314030964712288e-307 H3K27me3 Log2FC HB 0.2347875537574898 8.590917791342444e-160 H3K27me3 Log2FC SC 0.2956826706138225 1.5329233866307224e-256 H3K27me3 VAE0 -0.10520428908479008 7.957836150465658e-33 H3K27me3 VAE1 -0.8608600491616873 0.0 H3K27me3 VAE2 0.2279197055845889 1.8859458409143461e-150 Log2FC FB H3K4me1 -0.14880590787152698 2.8859404622978633e-64 Log2FC FB H3K4me2 -0.12969526458358954 3.9896551047753745e-49 Log2FC FB H3K4me3 -0.4470506488127457 0.0 Log2FC FB H3K27ac -0.3815669720025924 0.0 Log2FC FB H3K36me3 -0.4744138412763159 0.0 Log2FC FB H3K27me3 0.37643342244583206 0.0 Log2FC FB Log2FC FB 1.0 0.0 Log2FC FB Log2FC MB 0.7040491449224625 0.0 Log2FC FB Log2FC HB 0.5367783316680511 0.0 Log2FC FB Log2FC SC 0.5730464144717174 0.0 Log2FC FB VAE0 0.0035828141346172554 0.6852839419752368 Log2FC FB VAE1 -0.3026340104905353 3.046326192390388e-269 Log2FC FB VAE2 0.5758155364107505 0.0 Log2FC MB H3K4me1 -0.10387373575786396 4.872008964191265e-32 Log2FC MB H3K4me2 -0.09642120978180409 8.108800676805556e-28 Log2FC MB H3K4me3 -0.44737028826010944 0.0 Log2FC MB H3K27ac -0.4220883701842583 0.0 Log2FC MB H3K36me3 -0.43005987302358406 0.0 Log2FC MB H3K27me3 0.32254601068109157 1.314030964712587e-307 Log2FC MB Log2FC FB 0.7040491449224625 0.0 Log2FC MB Log2FC MB 0.9999999999999999 0.0 Log2FC MB Log2FC HB 0.7578212671301587 0.0 Log2FC MB Log2FC SC 0.5751592030701783 0.0 Log2FC MB VAE0 -0.17513450220728732 1.1229971545114095e-88 Log2FC MB VAE1 -0.21321910758293916 1.757463064410544e-131 Log2FC MB VAE2 0.46938929025701914 0.0 Log2FC HB H3K4me1 -0.05777615219411477 6.12378414825415e-11 Log2FC HB H3K4me2 -0.10550208768149451 5.2879602412044856e-33 Log2FC HB H3K4me3 -0.4197967924909555 0.0 Log2FC HB H3K27ac -0.408298389874337 0.0 Log2FC HB H3K36me3 -0.36341084086114817 0.0 Log2FC HB H3K27me3 0.2347875537574898 8.590917791342444e-160 Log2FC HB Log2FC FB 0.5367783316680511 0.0 Log2FC HB Log2FC MB 0.7578212671301588 0.0 Log2FC HB Log2FC HB 1.0 0.0 Log2FC HB Log2FC SC 0.47172347343211496 0.0 Log2FC HB VAE0 -0.3982180606696101 0.0 Log2FC HB VAE1 -0.07716379044745166 2.3055509300983664e-18 Log2FC HB VAE2 0.2883184296728832 1.8180376139160885e-243 Log2FC SC H3K4me1 -0.07950151137451963 2.1197279496875092e-19 Log2FC SC H3K4me2 -0.08178812465927764 1.9179743858494207e-20 Log2FC SC H3K4me3 -0.3297606127199917 0.0 Log2FC SC H3K27ac -0.2977765740809076 2.4919827852461e-260 Log2FC SC H3K36me3 -0.33209079328572233 0.0 Log2FC SC H3K27me3 0.2956826706138225 1.5329233866307224e-256 Log2FC SC Log2FC FB 0.5730464144717174 0.0 Log2FC SC Log2FC MB 0.5751592030701783 0.0 Log2FC SC Log2FC HB 0.47172347343211496 0.0 Log2FC SC Log2FC SC 0.9999999999999999 0.0 Log2FC SC VAE0 0.04095923910797624 3.570096858074452e-06 Log2FC SC VAE1 -0.24496989653146614 3.2652391791136445e-174 Log2FC SC VAE2 0.4631936937538506 0.0 VAE0 H3K4me1 -0.0446884001956768 4.251104865461492e-07 VAE0 H3K4me2 0.08834956848013696 1.335708453202751e-23 VAE0 H3K4me3 0.30597343061378124 1.8027240014084392e-275 VAE0 H3K27ac 0.3514588028990406 0.0 VAE0 H3K36me3 0.20757601112819188 1.4444036902704797e-124 VAE0 H3K27me3 -0.10520428908479008 7.957836150465658e-33 VAE0 Log2FC FB 0.003582814134617256 0.6852839419752368 VAE0 Log2FC MB -0.17513450220728735 1.1229971545113458e-88 VAE0 Log2FC HB -0.39821806066961 0.0 VAE0 Log2FC SC 0.04095923910797624 3.570096858074452e-06 VAE0 VAE0 1.0 0.0 VAE0 VAE1 -0.17727771491123703 7.574731590825203e-91 VAE0 VAE2 0.024102462909318584 0.006397261512370033 VAE1 H3K4me1 -0.08406049569917753 1.6476006172029343e-21 VAE1 H3K4me2 -0.2636875996916555 1.5735477679806943e-202 VAE1 H3K4me3 0.07296409905287946 1.4064868617096594e-16 VAE1 H3K27ac 0.05627016617662167 1.8912472752821038e-10 VAE1 H3K36me3 0.18686624881923297 6.647406888366817e-101 VAE1 H3K27me3 -0.8608600491616872 0.0 VAE1 Log2FC FB -0.30263401049053534 3.046326192389002e-269 VAE1 Log2FC MB -0.2132191075829392 1.757463064410544e-131 VAE1 Log2FC HB -0.07716379044745164 2.3055509300984496e-18 VAE1 Log2FC SC -0.2449698965314661 3.2652391791142007e-174 VAE1 VAE0 -0.17727771491123703 7.574731590825203e-91 VAE1 VAE1 1.0 0.0 VAE1 VAE2 0.015624110732809882 0.077162199979443 VAE2 H3K4me1 -0.19398340219121096 9.81715077576278e-109 VAE2 H3K4me2 -0.18498827221190545 6.86666738215701e-99 VAE2 H3K4me3 -0.5298862221619517 0.0 VAE2 H3K27ac -0.4482816481694506 0.0 VAE2 H3K36me3 -0.5117903743601302 0.0 VAE2 H3K27me3 0.2279197055845889 1.8859458409143461e-150 VAE2 Log2FC FB 0.5758155364107505 0.0 VAE2 Log2FC MB 0.46938929025701914 0.0 VAE2 Log2FC HB 0.2883184296728831 1.8180376139162956e-243 VAE2 Log2FC SC 0.4631936937538506 0.0 VAE2 VAE0 0.024102462909318584 0.006397261512370033 VAE2 VAE1 0.015624110732809882 0.077162199979443 VAE2 VAE2 1.0 0.0
<Figure size 216x216 with 0 Axes>
external_gene_name wt11fb_merged-rep wt13fb_merged-rep wt15fb_merged-rep \ 0 Pax2 2.002935 0.805872 0.687880 1 Hoxb9 0.479086 0.220758 0.272247 2 Hoxc9 0.070489 0.020139 0.114239 3 Hoxd9 0.018483 0.058725 0.189060 wt18fb_merged-rep wt11mb_merged-rep wt13mb_merged-rep wt15mb_merged-rep \ 0 0.319063 2.417627 3.253193 3.635322 1 0.172209 0.316455 0.084808 0.145274 2 0.111092 0.071978 0.022155 0.032423 3 0.075711 0.090560 0.000000 0.148719 wt18mb_merged-rep wt11hb_merged-rep wt13hb_merged-rep wt15hb_merged-rep \ 0 3.593899 5.158974 7.442453 6.735870 1 0.222129 0.583028 1.544333 2.252819 2 0.016071 0.118288 0.018302 0.081508 3 0.031184 0.035835 0.071966 0.072328 wt18hb_merged-rep wt11sc_merged-rep wt13sc_merged-rep wt15sc_merged-rep \ 0 6.124097 4.730448 7.405267 6.410930 1 3.759467 8.786930 9.736304 8.394440 2 1.539684 5.871115 6.350094 5.333934 3 0.048047 5.252176 3.308305 5.315772 wt18sc_merged-rep 0 6.355501 1 8.328319 2 5.571739 3 5.567879
external_gene_name ko11fb_merged-rep ko13fb_merged-rep ko15fb_merged-rep \ 0 Pax2 3.108827 5.765663 5.404836 1 Hoxb9 1.027615 3.872195 2.840668 2 Hoxc9 1.703283 6.086356 5.200582 3 Hoxd9 1.600412 5.909166 4.968913 ko18fb_merged-rep ko11mb_merged-rep ko13mb_merged-rep ko15mb_merged-rep \ 0 4.963471 3.909758 6.262265 5.467721 1 3.095592 2.442091 4.576879 2.964123 2 4.578695 2.760883 5.691054 4.920412 3 4.652042 2.412880 6.038424 4.916205 ko18mb_merged-rep ko11hb_merged-rep ko13hb_merged-rep ko15hb_merged-rep \ 0 4.525031 4.210750 6.481735 5.950515 1 2.827947 2.375786 6.178522 3.968796 2 4.175629 2.662632 5.024299 4.750092 3 4.574977 2.258272 5.722458 5.016718 ko18hb_merged-rep ko11sc_merged-rep ko13sc_merged-rep ko15sc_merged-rep \ 0 5.205509 4.984483 6.903416 6.332971 1 4.295223 9.149745 8.563543 7.179321 2 4.168916 6.278640 5.842700 5.185208 3 4.585869 5.649826 6.279059 5.465261 ko18sc_merged-rep 0 5.704444 1 6.969096 2 5.005286 3 5.319954
external_gene_name wt11fb_merged-rep wt13fb_merged-rep wt15fb_merged-rep \ 0 Otx2 5.758462 5.531050 4.405259 1 En2 0.494674 0.168652 0.353709 2 Phox2b 0.102214 0.132168 0.119700 3 Hoxd3 0.164494 0.133004 0.149337 wt18fb_merged-rep wt11mb_merged-rep wt13mb_merged-rep wt15mb_merged-rep \ 0 3.893296 7.722552 7.356560 6.650007 1 0.413756 7.499362 8.031306 7.013888 2 0.015052 2.805449 3.955419 3.151693 3 0.111092 0.140517 0.175505 1.068782 wt18mb_merged-rep wt11hb_merged-rep wt13hb_merged-rep wt15hb_merged-rep \ 0 5.799004 3.708020 1.019738 0.976341 1 6.668011 6.872836 1.465594 0.375820 2 2.330060 6.469630 7.075044 6.013573 3 0.123280 5.589150 8.062120 7.073398 wt18hb_merged-rep wt11sc_merged-rep wt13sc_merged-rep wt15sc_merged-rep \ 0 4.221625 0.527910 0.036119 0.063529 1 0.983470 0.548630 0.247523 0.205953 2 5.348831 3.407077 1.318731 0.177374 3 6.354484 6.996139 7.274181 5.681410 wt18sc_merged-rep 0 0.079962 1 0.233611 2 0.032084 3 5.317846
external_gene_name ko11fb_merged-rep ko13fb_merged-rep ko15fb_merged-rep \ 0 Otx2 7.054284 5.931086 5.888406 1 En2 4.821768 5.241948 4.639493 2 Phox2b 1.599360 1.065941 0.572130 3 Hoxd3 1.231702 5.185332 4.399199 ko18fb_merged-rep ko11mb_merged-rep ko13mb_merged-rep ko15mb_merged-rep \ 0 5.319730 5.748901 5.884490 5.933551 1 4.450940 6.970253 7.608890 6.280874 2 0.214009 4.499223 3.635362 1.911110 3 3.833791 3.028335 5.972110 4.999986 ko18mb_merged-rep ko11hb_merged-rep ko13hb_merged-rep ko15hb_merged-rep \ 0 5.312715 5.090304 4.275430 4.738128 1 6.268489 4.803762 5.565673 6.119783 2 2.076850 3.455354 5.766164 4.587203 3 3.755855 3.310889 5.867555 5.133170 ko18hb_merged-rep ko11sc_merged-rep ko13sc_merged-rep ko15sc_merged-rep \ 0 3.662862 0.820287 2.904100 2.846001 1 4.860775 3.372272 5.201905 4.665178 2 5.165144 2.972264 1.325385 1.776973 3 4.737131 7.106383 6.408114 5.623793 ko18sc_merged-rep 0 2.294013 1 4.312455 2 0.466172 3 5.027903
<Axes3DSubplot:xlabel='Node 1', ylabel='Node 2'>
<Figure size 360x360 with 0 Axes>
""" Print out the gene lists. """
gene_markers_sep = [['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6'],
['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4'],
['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b'],
['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11',
'Hoxa13', 'Hoxb9', 'Hoxb13', 'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13'],
['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a',
'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a'],
['Cdkn1a', 'Cdkn1b', 'Cdkn1c', 'Cdkn2a', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d'],
['Sox2', 'Sox1', 'Sox3', 'Hes1', 'Hes5'],
['Snap25', 'Syt1', 'Slc32a1','Slc17a6', 'Syn1'],
['Cspg4', 'Aqp4', 'Slc6a11', 'Olig1', 'Igfbp3'],
['Foxg1'],
['En2'],
['Phox2b'],
['Hoxc9'],
['Sox3']
]
marker_labels_sep = ['forebrain', 'midbrain', 'hindbrain', 'spinalcord',
'Proliferation', 'Negative regulators of Cell Cycle',
'progenitors', 'neurons', 'glia',
'Foxg1', 'En2', 'Phox2b', 'Hoxc9', 'Sox3'
]
# Print genes that were actually in our dataset
cx = [g for g in df[gene_name].values if g in ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11',
'Hoxa13', 'Hoxb9', 'Hoxb13', 'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']]
print(', '.join(cx))
len(cx)
cx = [g for g in df[gene_name].values if g in ['Sox2', 'Sox1', 'Sox3', 'Hes1', 'Hes5']]
print(', '.join(cx))
cx = [g for g in df[gene_name].values if g in ['Snap25', 'Syt1', 'Slc32a1','Slc17a6', 'Syn1']]
print(', '.join(cx))
cx = [g for g in df[gene_name].values if g in['Cspg4', 'Aqp4', 'Slc6a11', 'Olig1', 'Igfbp3']]
print(', '.join(cx))
prolif = ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a', 'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a']
anti_prolif = ['Cdkn1a', 'Cdkn1b', 'Cdkn1c', 'Cdkn2a', 'Cdkn2b', 'Cdkn2c', 'Cdkn2d']
pl = [g for g in df[gene_name].values if g in prolif]
apl = [g for g in df[gene_name].values if g in anti_prolif]
print(', '.join(pl))
print(', '.join(apl))
Hoxb13, Hoxb9, Hoxc13, Hoxc12, Hoxc11, Hoxc10, Hoxc9, Hoxc8, Hoxd13, Hoxd11, Hoxd10, Hoxd9, Hoxd8, Hoxa7, Hoxa9, Hoxa10, Hoxa11, Hoxa13 Hes1, Sox2, Hes5, Sox1, Sox3 Syt1, Snap25, Slc32a1, Slc17a6, Syn1 Igfbp3, Olig1, Aqp4, Slc6a11, Cspg4 Mcm3, E2f3, Cip2a, Ccnd3, Cdc25c, Mcm10, Cdc25b, E2f1, Ccna2, Ccna1, Ccne2, E2f2, Mcm2, Ccnd2, Ccne1, Ccnd1, Mcm5, Cdc25a Cdkn1a, Cdkn2a, Cdkn2b, Cdkn2c, Cdkn1b, Cdkn1c, Cdkn2d
We perform GSEA on the ranks of the VAE.
# -----------------------------------------------------------------------------------
# Save genes sorted by latent node (GSEA)
# -----------------------------------------------------------------------------------
for n in range(0, num_nodes):
with open(f'{ora_dir}vae{n}_genes_{experiment_name}_{date}.csv', 'w+') as f:
f.write(gene_id + ',value\n')
desc_sorted = (-vae_data[:,n]).argsort() # Sort the genes by descending order
genes = df[gene_id].values[desc_sorted]
vae_data_sorted = vae_data[:,n][desc_sorted]
i = 0
for g in genes:
f.write(f'{g},{vae_data_sorted[i]}\n')
i += 1
# -----------------------------------------------------------------------------------
# Save genes sorted by latent node (GSEA)
# -----------------------------------------------------------------------------------
for n in range(0, num_nodes):
with open(f'{ora_dir}vae{n}_gene-names_{experiment_name}_{date}.csv', 'w+') as f:
f.write(gene_name + ',value\n')
desc_sorted = (-vae_data[:,n]).argsort() # Sort the genes by descending order
genes = df[gene_name].values[desc_sorted]
vae_data_sorted = vae_data[:,n][desc_sorted]
i = 0
for g in genes:
f.write(f'{g},{vae_data_sorted[i]}\n')
i += 1
In the next section, we use information about the VAE nodes to compute clusters regarding the latent space.
def get_q1_q3(node_idx):
iqr_val = 0.95 * stats.iqr(vae_data[:, node_idx])
q1_genes_idxs = np.where(vae_data[:, node_idx] <= -1.25)[0] # (np.mean(vae_data[:, node_idx]) - iqr_val))[0]
q3_genes_idxs = np.where(vae_data[:, node_idx] >= 1.25)[0] #(np.mean(vae_data[:, node_idx]) + iqr_val))[0]
return q1_genes_idxs, q3_genes_idxs
# -----------------------------------------------------------------------------------
# Find the genes driving the latent space
# -----------------------------------------------------------------------------------
n_clusters = 6
# Get the indexs for each of the q1 and q3 for each node
q1_n0_idxs, q3_n0_idxs = get_q1_q3(0)
q1_n1_idxs, q3_n1_idxs = get_q1_q3(1)
q1_n2_idxs, q3_n2_idxs = get_q1_q3(2)
# Create a list of the indexs
vae_set_idxs = [
q1_n0_idxs, q3_n0_idxs,
q1_n1_idxs, q3_n1_idxs,
q1_n2_idxs, q3_n2_idxs
]
vae_set_labels = ['Set 1', 'Set 2', 'Set 3', 'Set 4', 'Set 5', 'Set 6']
# Get all the driver and non-driver idxs
non_driver_idxs = []
driver_idxs = []
for i, ids in enumerate(vae_set_idxs):
vae_mse.u.dp([vae_set_labels[i], len(ids)])
driver_idxs += list(ids)
for i in range(0, len(df)):
if i not in driver_idxs:
non_driver_idxs.append(i)
# -----------------------------------------------------------------------------------
# Plot a VENN of how the genes in teh clusters overlap
# -----------------------------------------------------------------------------------
gene_sets = [[], [], [], [], [], []]
for i, g in enumerate(df[gene_name].values):
ci = 0
for gene_set in vae_set_idxs:
for j in gene_set:
if i == j:
gene_sets[ci].append(g)
ci += 1
set_sets = []
for g in gene_sets:
if len(g) > 1:
set_sets.append(set(g))
set_labels = venn.get_labels(set_sets)
vae_set_labels_l = [s.replace('Set', 'Group') for s in vae_set_labels]
vae_set_genes_len_dict = {}
vae_set_genes_dict = {}
for i, l in enumerate(vae_set_labels):
vae_set_genes_dict[l] = gene_sets[i]
vae_set_genes_len_dict[l] = len(gene_sets[i])
venn.venn6(set_labels, names=vae_set_labels_l)
save_fig('gene_sets.svg')
plt.show()
vae_set_genes_as_list = []
for set_name in vae_set_labels:
vae_set_genes_as_list.append(vae_set_genes_dict.get(set_name))
sorted_sets_by_size = {k: v for k, v in sorted(vae_set_genes_len_dict.items(), key=lambda item: item[1])}
gene_set_labels_unique = []
for g in df[gene_name].values:
found = False
for label, values in sorted_sets_by_size.items():
gene_group = vae_set_genes_dict[label]
for g_l in gene_group:
if g_l == g:
gene_set_labels_unique.append(label)
found = True
break
if found:
break
if not found:
gene_set_labels_unique.append(None)
df['GeneDriverSet'] = gene_set_labels_unique
# We also want to add each of the gene sets
gene_labels = [[], [], [], [], [], []]
for g in df[gene_name].values:
for i, label in enumerate(vae_set_labels):
found = False
gene_group = vae_set_genes_dict[label]
for g_l in gene_group:
if g == g_l:
gene_labels[i].append(True)
found = True
break
if not found:
gene_labels[i].append(False)
for i, label in enumerate(vae_set_labels):
df[label] = gene_labels[i]
# -----------------------------------------------------------------------------------
# Plot the genes on a scatter plot
# -----------------------------------------------------------------------------------
vae_vis.plot_values_on_scatters(df, gene_name, vae_set_labels,
vae_set_genes_as_list, vae_data=vae_data, output_dir=fig_dir, plt_bg=True,
title=f'VAE gene driver sets {experiment_name}',
show_plt=True, save_fig=True)
# -----------------------------------------------------------------------------------
# Plot histograms of the VAE nodes with cutoffs used above
# -----------------------------------------------------------------------------------
for n in range(0, num_nodes):
x = vae_data[:,n]
iqr_val = 1.0 * stats.iqr(x)
plt.hist(vae_data[:,n], bins=20, color='grey')
plt.ylim(0, 380)
plt.axvline(x.mean() - iqr_val, color='k', linestyle='dashed', linewidth=1)
plt.axvline(x.mean() + iqr_val, color='r', linestyle='dashed', linewidth=1)
save_fig(f'hist-{n}')
plt.show()
-------------------------------------------------------------------------------- Set 1 1629 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Set 2 992 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Set 3 1789 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Set 4 486 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Set 5 849 -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Set 6 1722 --------------------------------------------------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/venn/_backwards_compatibility.py:15: UserWarning: `get_labels()` is retained for backwards compatibility; use `generate_petal_labels()` or the higher level `venn()` instead warn(( /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/venn/_backwards_compatibility.py:30: UserWarning: `venn6()` is retained for backwards compatibility; use `venn()` instead warn((
<Figure size 72x72 with 0 Axes>
# keep only unique genes and then see if there are any motifs in these groups
hox_genes = sc_genes
forebrain_genes = fb_genes
h3cols = []
fb_rna_cols = []
sc_rna_cols = []
# Do it in this way so they are correctly ordered
for c in df.columns:
if 'H3K27me' in c and 'signal' in c:
h3cols.append(c)
elif 'wt' in c:
if 'fb' in c:
fb_rna_cols.append(c)
if 'sc' in c:
sc_rna_cols.append(c)
for c in df.columns:
if 'ko' in c:
if 'fb' in c:
fb_rna_cols.append(c)
if 'sc' in c:
sc_rna_cols.append(c)
def plot_gene_boxplot(df, title, cluster_id, cols, idxs):
idxs = idxs[cluster_id]
boxplot = Boxplot(df, gene_name, cols[0])
box_df = boxplot.format_data_for_boxplot(df, cols, gene_name, df[gene_name].values[idxs])
is_hox = []
sns.set_style("white")
for c in box_df['Samples'].values:
if 'Hox' in c:
is_hox.append('Hox')
else:
is_hox.append('Hox like')
box_df['is_hox'] = is_hox
boxplot = Boxplot(box_df, "Conditions", "Values", add_stats=False, add_dots=False, figsize=(1,0.5),
order=cols)
boxplot.palette = sci_colour
ax = boxplot.plot()
plt.title(title)
ax.set_ylim(0, 12)
sns.set_style("white")
c = 0
for b in ax.artists:
if c < len(cols)/2:
b.set_facecolor(wt_colour)
else:
b.set_facecolor(ko_colour)
c += 1
save_fig(f'{title}')
plt.show()
avgs_fb = [['wt11fb1',
'wt11fb2'],
['wt13fb1',
'wt13fb2'],
['wt15fb1',
'wt15fb2'],
['wt18fb1',
'wt18fb2'],
['ko11fb1',
'ko11fb2'],
['ko13fb1',
'ko13fb2'],
['ko15fb1',
'ko15fb2'],
['ko18fb1',
'ko18fb2']]
avgs_sc = [['wt11sc1',
'wt11sc2'],
['wt13sc1',
'wt13sc2'],
['wt15sc1',
'wt15sc2'],
['wt18sc1',
'wt18sc2'],
['ko11sc1',
'ko11sc2'],
['ko13sc1',
'ko13sc2'],
['ko15sc1',
'ko15sc2'],
['ko18sc1',
'ko18sc2']]
avgs_hb = [['wt11hb1',
'wt11hb2'],
['wt13hb1',
'wt13hb2'],
['wt15hb1',
'wt15hb2'],
['wt18hb1',
'wt18hb2'],
['ko11hb1',
'ko11hb2'],
['ko13hb1',
'ko13hb2'],
['ko15hb1',
'ko15hb2'],
['ko18hb1',
'ko18hb2']]
avgs_mb = [['wt11mb1',
'wt11mb2'],
['wt13mb1',
'wt13mb2'],
['wt15mb1',
'wt15mb2'],
['wt18mb1',
'wt18mb2'],
['ko11mb1',
'ko11mb2'],
['ko13mb1',
'ko13mb2'],
['ko15mb1',
'ko15mb2'],
['ko18mb1',
'ko18mb2']]
avgs_df = pd.DataFrame()
avgs_df[gene_name] = df[gene_name].values
fb_cols = []
for f in avgs_fb:
new_col = f'{f[0][:2]} {f[0][2:4]} FB'
avgs_df[new_col] = (df[f[0]].values + df[f[1]].values) / 2.0
fb_cols.append(new_col)
sc_cols = []
for f in avgs_sc:
new_col = f'{f[0][:2]} {f[0][2:4]} SC'
avgs_df[new_col] = (df[f[0]].values + df[f[1]].values) / 2.0
sc_cols.append(new_col)
mb_cols = []
for f in avgs_mb:
new_col = f'{f[0][:2]} {f[0][2:4]} MB'
avgs_df[new_col] = (df[f[0]].values + df[f[1]].values) / 2.0
mb_cols.append(new_col)
hb_cols = []
for f in avgs_hb:
new_col = f'{f[0][:2]} {f[0][2:4]} HB'
avgs_df[new_col] = (df[f[0]].values + df[f[1]].values) / 2.0
hb_cols.append(new_col)
for i, gs in enumerate(vae_set_idxs):
plot_gene_boxplot(avgs_df, f'Cluster {i + 1} FB', i, fb_cols, vae_set_idxs)
plot_gene_boxplot(avgs_df, f'Cluster {i + 1} MB', i, mb_cols, vae_set_idxs)
plot_gene_boxplot(avgs_df, f'Cluster {i + 1} HB', i, hb_cols, vae_set_idxs)
plot_gene_boxplot(avgs_df, f'Cluster {i + 1} SC', i, sc_cols, vae_set_idxs)
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def plot_vae_rna_cols(vis_df, cols, num_nodes, fb_logfc, mb_logfc, hb_logfc, sc_logfc,
h3k27me3_fb_e11, h3k27me3_fb_e18, h3k27me3_hb_e11, h3k27me3_hb_e18, title='Heatmap',
num_values=10):
vae_cols = [f'VAE{i}' for i in range(0, num_nodes)]
sns.set_style("ticks")
for r_i, r_c in enumerate(cols):
for direction in ['up', 'down']:
# First add the bottom ones
fig, ax = plt.subplots()
sns.set(rc={'figure.figsize': (3.5, 2), 'font.family': 'sans-serif',
'font.sans-serif': 'Arial', 'font.size': 6}, style="ticks")
rcm_df_sorted = vis_df.sort_values(by=[r_c])
if direction == 'up':
rcm_df_tails = rcm_df_sorted.nlargest(num_values, r_c)
else:
rcm_df_tails = rcm_df_sorted.nsmallest(num_values, r_c)
# Add the smallest and the largest
# first heatmap
N = len(rcm_df_tails)
ax.set_xlim(-0.5, 7.5)
ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7])
ax.set_xticklabels(['FB', 'MB', 'HB', 'SC',
'FB e11.5', 'FB e18.5', 'HB e11.5', 'HB e18'])
ax.set_yticks(range(N))
ax.set_yticklabels(list(rcm_df_tails[gene_name].values))
ax.set_ylabel('Genes')
max_hk3 = 35
max_rna = 10
min_rna = -10
cmap_rna = 'seismic'
cmap_hk3 = 'Greens'
im1 = ax.imshow(np.vstack([rcm_df_tails[fb_logfc], rcm_df_tails[fb_logfc]]).T,
aspect='auto',
extent=[-0.5, 0.5, -0.5, N - 0.5], vmin=min_rna, vmax=max_rna,
origin='lower', cmap=cmap_rna)
im1 = ax.imshow(np.vstack([rcm_df_tails[mb_logfc], rcm_df_tails[mb_logfc]]).T,
aspect='auto',
extent=[0.5, 1.5, -0.5, N - 0.5], vmin=min_rna, vmax=max_rna,
origin='lower', cmap=cmap_rna)
im1 = ax.imshow(np.vstack([rcm_df_tails[hb_logfc], rcm_df_tails[hb_logfc]]).T,
aspect='auto',
extent=[1.5, 2.5, -0.5, N - 0.5], vmin=min_rna, vmax=max_rna,
origin='lower', cmap=cmap_rna)
# Now add in the H3K27me3 ones
im1 = ax.imshow(np.vstack([rcm_df_tails[sc_logfc], rcm_df_tails[sc_logfc]]).T,
aspect='auto',
extent=[2.5, 3.5, -0.5, N - 0.5], vmin=min_rna, vmax=max_rna,
origin='lower', cmap=cmap_rna)
im3 = ax.imshow(np.vstack([rcm_df_tails[h3k27me3_fb_e11], rcm_df_tails[h3k27me3_fb_e11]]).T,
aspect='auto',
extent=[3.5, 4.5, -0.5, N - 0.5], vmin=0, vmax=max_hk3,
origin='lower', cmap=cmap_hk3)
im3 = ax.imshow(np.vstack([rcm_df_tails[h3k27me3_fb_e18], rcm_df_tails[h3k27me3_fb_e18]]).T,
aspect='auto',
extent=[4.5, 5.5, -0.5, N - 0.5], vmin=0, vmax=max_hk3,
origin='lower', cmap=cmap_hk3)
im3 = ax.imshow(np.vstack([rcm_df_tails[h3k27me3_hb_e11], rcm_df_tails[h3k27me3_hb_e11]]).T,
aspect='auto',
extent=[5.5, 6.5, -0.5, N - 0.5], vmin=0, vmax=max_hk3,
origin='lower', cmap=cmap_hk3)
im3 = ax.imshow(np.vstack([rcm_df_tails[h3k27me3_hb_e18], rcm_df_tails[h3k27me3_hb_e18]]).T,
aspect='auto',
extent=[6.5, 7.5, -0.5, N - 0.5], vmin=0, vmax=max_hk3,
origin='lower', cmap=cmap_hk3)
cbar1 = fig.colorbar(im1, ax=ax, label='RNA logFC')
cbar2 = fig.colorbar(im3, ax=ax, label='H3K27me3 signal')
fig.tight_layout()
plt.xticks(rotation=45, horizontalalignment='right')
plt.title(f'{title} {r_c}')
save_fig(f'{title.replace(" ", "_")}_{direction}_{r_c}')
h3k27me3_fb_e11 = [c for c in df.columns if '10.5' in c and 'forebrain' in c and 'H3K27me3' in c and 'signal' in c and 'median' not in c][0]
h3k27me3_fb_e18 = [c for c in df.columns if '16.5' in c and 'forebrain' in c and 'H3K27me3' in c and 'signal' in c and 'median' not in c][0]
h3k27me3_hb_e11 = [c for c in df.columns if '10.5' in c and 'hindbrain' in c and 'H3K27me3' in c and 'signal' in c and 'median' not in c][0]
h3k27me3_hb_e18 = [c for c in df.columns if '16.5' in c and 'hindbrain' in c and 'H3K27me3' in c and 'signal' in c and 'median' not in c][0]
plot_vae_rna_cols(df, ["VAE0", "VAE1", "VAE2"], 3,
'Log2FC FB',
'Log2FC MB',
'Log2FC HB',
'Log2FC SC',
h3k27me3_fb_e11, h3k27me3_fb_e18, h3k27me3_hb_e11, h3k27me3_hb_e18)
<ipython-input-24-741904c72e35>:75: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. fig.tight_layout()
# -----------------------------------------------------------------------------------
# Save background genes (ORA)
# -----------------------------------------------------------------------------------
with open(f'{ora_dir}background_genes_{experiment_name}_{date}.csv', 'w+') as f:
genes = df_all[gene_id].values
f.write(gene_id + '\n')
for g in genes:
f.write(f'{g}\n')
# -----------------------------------------------------------------------------------
# Save training genes (sig genes) (ORA)
# -----------------------------------------------------------------------------------
with open(f'{ora_dir}training-df_genes_{experiment_name}_{date}.csv', 'w+') as f:
genes = df_training[gene_id].values
f.write(gene_id + '\n')
for g in genes:
f.write(f'{g}\n')
# -----------------------------------------------------------------------------------
# Save genes in each of the clusters we annotated (ORA)
# -----------------------------------------------------------------------------------
for i in range(0, n_clusters):
with open(f'{ora_dir}cluster_{i}_genes_{experiment_name}_{date}.csv', 'w+') as f:
genes = df[gene_id].values[vae_set_idxs[i]]
f.write(gene_id + '\n')
for g in genes:
f.write(f'{g}\n')
# -----------------------------------------------------------------------------------
# Save genes sorted by latent node (GSEA) and only the top
# -----------------------------------------------------------------------------------
def plot_top_vae_genes(vae_data_sorted, gene_ids_sorted, gene_names_sorted, label="", num=20):
i = 0
gs = []
for g in gene_names_sorted:
if g != "0":
f.write(f'{g},{vae_data_sorted[i]}\n')
gs.append(g)
else:
f.write(f'{gene_ids_sorted[i]},{vae_data_sorted[i]}\n')
gs.append(gene_ids_sorted[i])
i += 1
if i >= num:
break
heatmap_df = pd.DataFrame()
heatmap_df[gene_name] = gs
heatmap_df['values'] = vae_data_sorted[:num]
heatmap = Heatmap(heatmap_df, ['values'], gene_name, vmin=-4, vmax=4,
title=f'top {num} genes', cluster_cols=False, cluster_rows=False)
heatmap.palette=sns.color_palette(sci_colour)
heatmap.plot()
pplot()
save_fig(f'vae{n}_genes-top-{num}-{label}')
plt.show()
for n in range(0, num_nodes):
with open(f'{ora_dir}vae-{n + 1}_genes-top10_{experiment_name}_{date}.csv', 'w+') as f:
f.write(gene_id + ',value\n')
desc_sorted = (-vae_data[:,n]).argsort() # Sort the genes by descending order
gene_names_sorted = df[gene_name].values[desc_sorted]
gene_ids_sorted = df[gene_id].values[desc_sorted]
vae_data_sorted = vae_data[:,n][desc_sorted]
plot_top_vae_genes(vae_data_sorted, gene_ids_sorted, gene_names_sorted, label="top")
with open(f'{ora_dir}vae-{n + 1}_genes-bottom10_{experiment_name}_{date}.csv', 'w+') as f:
f.write(gene_id + ',value\n')
desc_sorted = (vae_data[:,n]).argsort() # Sort the genes by descending order
gene_names_sorted = df[gene_name].values[desc_sorted]
vae_data_sorted = vae_data[:,n][desc_sorted]
gene_ids_sorted = df[gene_id].values[desc_sorted]
plot_top_vae_genes(vae_data_sorted, gene_ids_sorted, gene_names_sorted, label="bottom")
for n in range(0, num_nodes):
with open(f'{ora_dir}vae-{n + 1}_{experiment_name}_{date}.csv', 'w+') as f:
f.write(gene_id + ',value\n')
desc_sorted = (vae_data[:,n]).argsort() # Sort the genes by descending order
gene_names_sorted = df[gene_id].values[desc_sorted]
vae_data_sorted = vae_data[:,n][desc_sorted]
for i, g in enumerate(gene_names_sorted):
f.write(f'{g},{vae_data_sorted[i]}\n')
# -----------------------------------------------------------------------------------
# Save genes in each of the clusters we annotated (ORA)
# -----------------------------------------------------------------------------------
for i in range(0, n_clusters):
with open(f'{ora_dir}cluster_{i}_gene-names_{experiment_name}_{date}.csv', 'w+') as f:
genes = df[gene_name].values[vae_set_idxs[i]]
rank_vae0 = vae_data[:, 0][vae_set_idxs[i]]
rank_vae1 = vae_data[:, 1][vae_set_idxs[i]]
rank_vae2 = vae_data[:, 2][vae_set_idxs[i]]
f.write(f'gene_name,VAE-1,VAE-2,VAE-3\n')
for i, g in enumerate(genes):
f.write(f'{g},{rank_vae0[i]},{rank_vae1[i]},{rank_vae2[i]}\n')
import string
tissues = ['forebrain', 'midbrain', 'hindbrain', 'neural-tube',
'embryonic-facial-prominence', 'limb',
'heart', 'liver']
marks = ['H3K9me3', 'H3K36me3', 'H3K27me3', 'H3K27ac', 'H3K4me1', 'H3K4me2', 'H3K4me3','H3K9ac']
def get_median_naninc(df, mark, hist_metric="", time_1="", time_2="", time_3="", tissue="brain"):
cols = []
for c in df.columns:
if tissue in c and mark in c and hist_metric in c and (time_1 in c or time_2 in c or time_3 in c):
cols.append(c)
# get nan median
vals = np.nanmedian(df[cols].values, axis=1)
return vals
def plot_mark_heatmap(df, idxs, title, mark_cutoff=3.0):
mark_df = pd.DataFrame()
mark_values = []
mean_col = []
if idxs is None:
num_genes = len(df)
else:
num_genes = len(idxs) * 1.0
for t in [['10.5', '11.5', '12.5'], ['15.5', '15.5', '16.5']]:
tissue_titles = []
marks_title = []
for m in marks:
mark_col = []
for tissue in tissues:
median_all_data = get_median_naninc(df, m, "signal", t[0], t[1], t[2], tissue)
if idxs is None:
median_genes = median_all_data
else:
median_genes = median_all_data[idxs]
has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
mark_col.append(has_mark/num_genes) #np.nan_to_num(np.nanmean(median_genes)))
if string.capwords(tissue) not in tissue_titles:
if tissue == 'embryonic-facial-prominence':
if 'E.F.P' not in tissue_titles:
tissue_titles.append('E.F.P')
else:
tissue_titles.append(string.capwords(tissue))
marks_title.append(string.capwords(m))
mark_df[string.capwords(m)] = mark_col
mark_df['Tissue'] = tissue_titles
heatmap = Heatmap(mark_df, marks_title, 'Tissue', vmin=0, vmax=None, cmap=hist_cmap, figsize=(2,2),
title=f'{title} {",".join(t)}', cluster_rows=False, cluster_cols=False)
heatmap.plot()
pplot()
print(mark_df.head())
# vmin=0, vmax=1,
save_fig(f'mark_all_tissues_signal-{title}_{"-".join(t)}')
plt.show()
for i, gs in enumerate(vae_set_idxs):
plot_mark_heatmap(df, gs, f'Cluster {i}')
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.087170 0.242480 0.553714 0.435236 0.663597 0.799263 0.799263
1 0.071209 0.257213 0.572130 0.408840 0.683241 0.790055 0.799877
2 0.057090 0.192142 0.573358 0.434009 0.591160 0.791283 0.805402
3 0.079804 0.208103 0.497238 0.266421 0.651320 0.825660 0.785758
4 0.174340 0.352363 0.593616 0.660528 0.732965 0.835482 0.841007
H3k9ac Tissue
0 0.631676 Forebrain
1 0.638428 Midbrain
2 0.606507 Hindbrain
3 0.728054 Neural-tube
4 0.752609 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.075506 0.107428 0.464702 0.260282 0.718232 0.765500 0.778392
1 0.098834 0.112339 0.534070 0.278085 0.666667 0.810927 0.782075
2 0.093923 0.130755 0.495396 0.307551 0.717004 0.823818 0.787600
3 0.043585 0.076734 0.438920 0.316145 0.719460 0.820135 0.785758
4 0.038060 0.167587 0.360958 0.467772 0.611418 0.831799 0.815838
H3k9ac Tissue
0 0.697974 Forebrain
1 0.718232 Midbrain
2 0.718232 Hindbrain
3 0.627379 Neural-tube
4 0.569061 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.154234 0.384073 0.320565 0.767137 0.632056 0.895161 0.902218
1 0.162298 0.439516 0.338710 0.801411 0.622984 0.900202 0.902218
2 0.123992 0.339718 0.281250 0.824597 0.437500 0.900202 0.903226
3 0.133065 0.409274 0.197581 0.703629 0.491935 0.923387 0.900202
4 0.274194 0.421371 0.535282 0.790323 0.698589 0.911290 0.911290
H3k9ac Tissue
0 0.853831 Forebrain
1 0.863911 Midbrain
2 0.851815 Hindbrain
3 0.895161 Neural-tube
4 0.842742 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.196573 0.441532 0.038306 0.850806 0.717742 0.898185 0.915323
1 0.213710 0.551411 0.010081 0.883065 0.614919 0.923387 0.914315
2 0.192540 0.569556 0.004032 0.894153 0.605847 0.932460 0.918347
3 0.096774 0.312500 0.009073 0.901210 0.619960 0.928427 0.912298
4 0.062500 0.203629 0.246976 0.507056 0.581653 0.898185 0.887097
H3k9ac Tissue
0 0.912298 Forebrain
1 0.913306 Midbrain
2 0.919355 Hindbrain
3 0.890121 Neural-tube
4 0.675403 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.114589 0.190050 0.956400 0.495808 0.814980 0.940190 0.951369
1 0.110117 0.222471 0.969816 0.499162 0.798770 0.935159 0.950811
2 0.091671 0.169927 0.953605 0.556736 0.684181 0.947457 0.957518
3 0.111235 0.222471 0.831191 0.411403 0.751817 0.969816 0.955841
4 0.210732 0.295696 0.968698 0.702627 0.840693 0.955282 0.965903
H3k9ac Tissue
0 0.776970 Forebrain
1 0.787591 Midbrain
2 0.787591 Hindbrain
3 0.901621 Neural-tube
4 0.851314 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.132476 0.191727 0.642817 0.447177 0.830632 0.909447 0.935718
1 0.160425 0.273337 0.638345 0.548351 0.732812 0.951369 0.941308
2 0.135830 0.282281 0.592510 0.594746 0.787591 0.964226 0.953605
3 0.069871 0.153158 0.536054 0.590274 0.808832 0.967021 0.954164
4 0.054779 0.139743 0.638904 0.422023 0.763555 0.948016 0.946339
H3k9ac Tissue
0 0.869201 Forebrain
1 0.879821 Midbrain
2 0.892119 Hindbrain
3 0.833426 Neural-tube
4 0.633874 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.072016 0.246914 0.000000 0.327160 0.302469 0.395062 0.382716
1 0.076132 0.244856 0.000000 0.343621 0.331276 0.386831 0.386831
2 0.057613 0.166667 0.000000 0.355967 0.251029 0.384774 0.399177
3 0.076132 0.191358 0.002058 0.251029 0.323045 0.436214 0.374486
4 0.174897 0.300412 0.002058 0.452675 0.407407 0.452675 0.438272
H3k9ac Tissue
0 0.343621 Forebrain
1 0.358025 Midbrain
2 0.333333 Hindbrain
3 0.378601 Neural-tube
4 0.399177 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.069959 0.121399 0.006173 0.267490 0.351852 0.374486 0.380658
1 0.127572 0.125514 0.000000 0.290123 0.327160 0.434156 0.382716
2 0.094650 0.133745 0.000000 0.300412 0.372428 0.452675 0.390947
3 0.059671 0.104938 0.000000 0.325103 0.351852 0.442387 0.386831
4 0.030864 0.148148 0.002058 0.376543 0.267490 0.467078 0.432099
H3k9ac Tissue
0 0.374486 Forebrain
1 0.390947 Midbrain
2 0.384774 Hindbrain
3 0.339506 Neural-tube
4 0.333333 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.074205 0.829211 0.167256 0.889282 0.640754 0.937574 0.950530
1 0.062426 0.838634 0.188457 0.909305 0.667845 0.938751 0.950530
2 0.047114 0.743227 0.175501 0.921084 0.517079 0.941107 0.950530
3 0.054181 0.769140 0.129564 0.845701 0.571260 0.951708 0.945819
4 0.140165 0.865724 0.214370 0.945819 0.680801 0.955241 0.955241
H3k9ac Tissue
0 0.908127 Forebrain
1 0.908127 Midbrain
2 0.897527 Hindbrain
3 0.938751 Neural-tube
4 0.942285 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.062426 0.611307 0.153121 0.826855 0.793875 0.904594 0.944641
1 0.055359 0.577150 0.186101 0.812721 0.718492 0.951708 0.944641
2 0.044759 0.645465 0.159011 0.833922 0.747939 0.949352 0.945819
3 0.023557 0.414605 0.134276 0.853946 0.758539 0.948174 0.938751
4 0.040047 0.552415 0.090695 0.883392 0.565371 0.950530 0.945819
H3k9ac Tissue
0 0.937574 Forebrain
1 0.926973 Midbrain
2 0.928151 Hindbrain
3 0.890459 Neural-tube
4 0.897527 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.110918 0.087689 0.333914 0.266551 0.465738 0.567364 0.556330
1 0.105691 0.087108 0.342625 0.272358 0.472706 0.562718 0.559233
2 0.093496 0.073171 0.322880 0.299071 0.368177 0.569686 0.565621
3 0.105110 0.088850 0.239837 0.173055 0.410569 0.621370 0.548780
4 0.204994 0.135308 0.451800 0.417538 0.581882 0.619048 0.613821
H3k9ac Tissue
0 0.447154 Forebrain
1 0.445993 Midbrain
2 0.427410 Hindbrain
3 0.531359 Neural-tube
4 0.497677 E.F.P
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/numpy/lib/nanfunctions.py:1115: RuntimeWarning: All-NaN slice encountered r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, <ipython-input-28-d9013eae8312>:34: RuntimeWarning: invalid value encountered in greater has_mark = 1.0 * len(np.where(median_genes > mark_cutoff)[0])
H3k9me3 H3k36me3 H3k27me3 H3k27ac H3k4me1 H3k4me2 H3k4me3 \
0 0.134727 0.082462 0.128339 0.307201 0.515099 0.582462 0.571429
1 0.188153 0.164925 0.106852 0.398374 0.407666 0.622532 0.584204
2 0.170732 0.148664 0.079559 0.418699 0.457027 0.641696 0.598722
3 0.088850 0.090012 0.097561 0.402439 0.472706 0.644599 0.589431
4 0.063298 0.070267 0.208479 0.231127 0.444251 0.632985 0.595819
H3k9ac Tissue
0 0.545296 Forebrain
1 0.563879 Midbrain
2 0.575494 Hindbrain
3 0.513357 Neural-tube
4 0.313589 E.F.P
Downloaded the pooled samples frmo http://enhancer.sdsc.edu/enhancer_export/ENCODE/chromHMM/pooled/ for forebrain, hindbrain, and midbrian at e16.5 on 04 December 2020.
http://enhancer.sdsc.edu/enhancer_export/ENCODE/chromHMM/readme
The files in this directory contain chromHMM chromatin state calls for multiple tissues during mouse embryonic development, as described here: http://www.biorxiv.org/content/early/2017/07/21/166652.
All files are in bed format. There is one file for each tissue at each developmental stage. The columns are 1) chromosome, 2) regions start, 3) region end, 4) chromatin state number 5) Chromatin state label. There are 15 chromating states, labeled as follows:
State Label Description
1 Pr-A Promoter, Active
2 Pr-W Promoter, Weak/Inactive
3 Pr-B Promoter, Bivalent
4 Pr-F Promoter, Flanking
5 En-Sd Enhancer, Strong TSS-distal
6 En-Sp Enhancer, Strong TSS-proximal
7 En-W Enhancer, Weak TSS-distal
8 En-Pd Enhancer, Poised TSS-distal
9 En-Pp Enhancer, Poised TSS-proximal
10 Tr-S Transcription, Strong
11 Tr-P Transcription, Permissive
12 Tr-I Transcription, Initiation
13 Hc-P Heterochromatin, Polycomb-associated
14 Hc-H Heterochromatin, H3K9me3-associated
15 NS No Chromatin Signal
NRS NRS No Reproducible State (ony relevant to "replicated" call set)
There are four subdirectories:
rep1\ This subdirectory contains chromatin state calls made using ChIP-seq datasets from biological replicate 1 (two biological replicates were performed for each ChIP-seq experiment).
rep2\ This subdirectory contains chromatin state calls made using ChIP-seq datasets from biological replicate 2.
replicated\ This subdirectory contains the intersection of the replicate 1 and replicate 2 chromatin state calls. Here, we require that a region is called in the same state in both replicates. If a region is not called in the same state in both replicates, it is labeled "NRS" for No Reproducible Signal. Note this is differenct than state 15, which is "No Signal."
pooled\ This subdirectory contains chromatin state calls on ChIP-seq data pooled from both replicates. We expect this will be the desired set of files for most users.
Each subdirectory has two additional subdirectorites:
archive\ This contains an older version of the bed files in which there are only state number, no labels. Aside from the labels, the files are identical to the working versions in the main directory.
tracks\ This contains bigBed versions of the files for visualization on the UCSC Genome Browser.
There is one additional subdirectory that does not conform to the templates decribed above. This is 6_mark_models. This subdirectory contains chromHMM segmentation of the e10.5 stage where we did not have all 8 marks. Note that the 11-state and 16-states models used for this segmentation are distinct from the 15-state (8-mark) model described above for the other stages. Please see manuscript and Extended Data Figure 7 for additional information.
header = "chr,start,end,label,annotation"
bed = Bed(os.path.join(self.data_dir, 'e16.5_forebrain_15_segments.bed'),
header=None, overlap_method='in_promoter',
output_bed_file=os.path.join(self.data_dir, 'e16.5_forebrain_15_segments_selectedPeaks.bed'),
buffer_after_tss=0, buffer_before_tss=10, buffer_gene_overlap=0,
gene_start=3, gene_end=4, gene_chr=2, gene_direction=5, gene_name=0,
chr_idx=0, start_idx=1, end_idx=2, peak_value=4, header_extra="3", sep='\t'
)
# Add the gene annot
bed.set_annotation_from_file(self.mm10_annot)
# Now we can run the assign values
bed.assign_locations_to_genes()
bed.save_loc_to_csv(f'{self.data_dir}test_e16.5_forebrain_15_segments.csv')
# -----------------------------------------------------------------------------------
# Merge information with annotations
# -----------------------------------------------------------------------------------
chrom_dir = os.path.join(supp_dir, "annot")
chromhmm_annot = pd.read_csv(f'{chrom_dir}/e16.5_forebrain_15_segments.csv')
print(len(chromhmm_annot), len(df))
# Make sure we only keep 1 annotation per gene
chromhmm_annot = chromhmm_annot.groupby('external_gene_name').first()
# Merge this with our dataframe
df = df.merge(chromhmm_annot, on='external_gene_name', how='left', suffixes=('', '_chmm'))
# Ensure the new length is the same as the old length
len(df)
53254 12797
12797
# We also want to plot the annotations from gorkin et al for each of the clusters
from statsmodels.stats.multitest import multipletests
def run_annot_plot(df_bg, df_fg, title=""):
changes = []
order = ['Pr-A', 'Pr-W', 'Pr-B', 'Pr-F', 'En-Sd', 'En-Sp',
'En-W', 'En-Pd', 'En-Pp', 'Tr-S', 'Tr-P', 'Tr-I',
'Hc-P', 'Hc-H', 'NS']
total = len(df_bg) * 1.0
total_sig = len(df_fg) * 1.0
perc_all = []
perc_sig = []
for o in order:
o_all = len(df_bg[df_bg['peak_value'] == o])
o_sig = len(df_fg[df_fg['peak_value'] == o])
perc_all.append(o_all)
perc_sig.append(o_sig)
if o_all == 0 and o_sig == 0:
changes.append(0)
else:
changes.append(((o_sig) - (o_all)))
odds_ratios = []
pvalues = []
for i, o in enumerate(order):
# Do a FET on each one
oddsratio, pvalue = stats.fisher_exact([[perc_sig[i], perc_all[i]],
[total_sig - perc_sig[i],
total - perc_all[i]]])
print(o, oddsratio, pvalue,[[perc_sig[i], perc_all[i]],
[total_sig - perc_sig[i],
total - perc_all[i]]])
odds_ratios.append(oddsratio)
pvalues.append(pvalue)
reg, padj, a, b = multipletests(pvalues, alpha=0.1,
method='fdr_bh', returnsorted=False)
p_sigs = []
for p in padj:
if p > 0.05:
p_sigs.append('')
elif p <= 0.05 and p > 0.01:
p_sigs.append('*')
elif p <= 0.01 and p > 0.001:
p_sigs.append('**')
elif p <= 0.001 and p > 0.0001:
p_sigs.append('***')
elif p <= 0.0001:
p_sigs.append('****')
else:
print(p)
p_sigs.append('')
fig, ax = plt.subplots(figsize=(1.5,1.0))
c = [grey] * 14
c[2] = "green"
c[12] = "green"
plt.bar(order, odds_ratios, color=c, linewidth=0.5, edgecolor='black')
rects = ax.patches
# Make some labels.
labels = [f'{p_sigs[i]}' for i in range(0, len(rects))]
for rect, label in zip(rects, labels):
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2, height, label,
ha='center', va='bottom')
plt.ylim(0, 7)
plt.yticks(np.arange(0, 7, 2.0))
ax.set_title(f'Odds ratio FET for significant dataset {title} compared to all genes ChromHMM annotations')
ax.set_xlabel('', fontsize=6)
ax.set_ylabel('Odds ratio', fontsize=6)
ax.set_xticklabels(order, rotation=90, ha="center")
ax.tick_params(direction='out', length=2, width=0.5)
ax.spines['bottom'].set_linewidth(0.5)
ax.spines['top'].set_linewidth(0)
ax.spines['left'].set_linewidth(0.5)
ax.spines['right'].set_linewidth(0)
ax.tick_params(labelsize=6)
ax.tick_params(axis='x', which='major', pad=0)
ax.tick_params(axis='y', which='major', pad=0)
save_fig(f'OR-CM-{title}')
plt.show()
for gene_set_label in vae_set_labels:
run_annot_plot(df, df[df[gene_set_label] == True], title=f'Training vs {gene_set_label}')
Pr-A 0.1544014959515061 4.34514783870178e-152 [[148, 5022], [1484.0, 7775.0]] Pr-W 0.6797084357379283 1.0567936898432639e-08 [[276, 2949], [1356.0, 9848.0]] Pr-B 5.612359685520856 4.11518212909669e-124 [[427, 760], [1205.0, 12037.0]] Pr-F 1.8964525100572018 4.578507599149844e-14 [[215, 948], [1417.0, 11849.0]] En-Sd 1.0357968069666184 0.8392198552759471 [[7, 53], [1625.0, 12744.0]] En-Sp 0.577705101361476 0.008453934161838345 [[23, 309], [1609.0, 12488.0]] En-W 1.3074483323102108 0.7252071657333816 [[3, 18], [1629.0, 12779.0]] En-Pd 1.1636798088410991 0.5231419550696941 [[20, 135], [1612.0, 12662.0]] En-Pp 0.8121371290594385 0.3320318548288891 [[31, 298], [1601.0, 12499.0]] Tr-S 0.6913118838695699 0.7938988765577057 [[3, 34], [1629.0, 12763.0]] Tr-P 1.19962391587996 0.08046413762297064 [[117, 774], [1515.0, 12023.0]] Tr-I 1.309921617161716 0.2958836066183933 [[16, 96], [1616.0, 12701.0]] Hc-P 4.624108676666762 3.1683856209629525e-41 [[154, 282], [1478.0, 12515.0]] Hc-H 0.9561215464643891 1.0 [[5, 41], [1627.0, 12756.0]] NS 1.5910870927318295 1.841602087210621e-05 [[120, 608], [1512.0, 12189.0]]
<ipython-input-30-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 1.7905130469412844 1.5470479661405515e-18 [[532, 5022], [460.0, 7775.0]] Pr-W 0.7757450151541 0.0021551619127950896 [[187, 2949], [805.0, 9848.0]] Pr-B 0.06412209673982527 5.064232407196575e-20 [[4, 760], [988.0, 12037.0]] Pr-F 0.7903941733259165 0.09920551551373546 [[59, 948], [933.0, 11849.0]] En-Sd 2.201500988502658 0.04199143858146963 [[9, 53], [983.0, 12744.0]] En-Sp 2.787188929806941 6.473298086571745e-11 [[64, 309], [928.0, 12488.0]] En-W 0.7163919721941922 1.0 [[1, 18], [991.0, 12779.0]] En-Pd 0.955118050841065 1.0 [[10, 135], [982.0, 12662.0]] En-Pp 0.731313027017725 0.2685249750118035 [[17, 298], [975.0, 12499.0]] Tr-S 1.1386724558377446 0.747118635436065 [[3, 34], [989.0, 12763.0]] Tr-P 0.6867482660138718 0.017520939051845773 [[42, 774], [950.0, 12023.0]] Tr-I 0.5356359649122807 0.3268296930311487 [[4, 96], [988.0, 12701.0]] Hc-P 0.0 1.281025909130033e-09 [[0, 282], [992.0, 12515.0]] Hc-H 0.31394747852624844 0.3662629728070885 [[1, 41], [991.0, 12756.0]] NS 0.5395860575351422 0.0012116728848613032 [[26, 608], [966.0, 12189.0]]
<ipython-input-30-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 0.36170621834568834 4.893376057636945e-68 [[339, 5022], [1451.0, 7775.0]] Pr-W 0.6994766893037493 2.814266760798898e-08 [[310, 2949], [1480.0, 9848.0]] Pr-B 7.476235716759002 2.865486203859511e-200 [[574, 760], [1216.0, 12037.0]] Pr-F 1.7333142762048936 4.184071057664497e-11 [[218, 948], [1572.0, 11849.0]] En-Sd 0.8086978593789661 0.8417314640702478 [[6, 53], [1784.0, 12744.0]] En-Sp 1.3533636778104143 0.04350726593939813 [[58, 309], [1732.0, 12488.0]] En-W 0.39683870567045526 0.7217593777467967 [[1, 18], [1789.0, 12779.0]] En-Pd 0.3154459392127554 0.0017720931104120668 [[6, 135], [1784.0, 12662.0]] En-Pp 0.3544474903109935 7.629987513721038e-06 [[15, 298], [1775.0, 12499.0]] Tr-S 0.0 0.017729304713158322 [[0, 34], [1790.0, 12763.0]] Tr-P 0.15779043519344849 1.7859101219573136e-25 [[18, 774], [1772.0, 12023.0]] Tr-I 0.6685675182481752 0.29646604752793904 [[9, 96], [1781.0, 12701.0]] Hc-P 4.839392900770738 5.228450373230861e-48 [[176, 282], [1614.0, 12515.0]] Hc-H 0.1739083014083355 0.05575369087750716 [[1, 41], [1789.0, 12756.0]] NS 0.0 6.954291297050197e-36 [[0, 608], [1790.0, 12189.0]]
<ipython-input-30-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 0.185070746188037 1.0435068214210773e-43 [[52, 5022], [435.0, 7775.0]] Pr-W 0.5793722192942676 9.800765623440644e-06 [[72, 2949], [415.0, 9848.0]] Pr-B 0.0 3.924004855628132e-13 [[0, 760], [487.0, 12037.0]] Pr-F 0.5074358072775794 0.002385388983424292 [[19, 948], [468.0, 11849.0]] En-Sd 0.9915580626337288 1.0 [[2, 53], [485.0, 12744.0]] En-Sp 0.6749768598279857 0.361820624118778 [[8, 309], [479.0, 12488.0]] En-W 1.4607910379515319 0.5084204535357826 [[1, 18], [486.0, 12779.0]] En-Pd 3.3924980299448384 4.191641059531434e-05 [[17, 135], [470.0, 12662.0]] En-Pp 0.9692699227341943 1.0 [[11, 298], [476.0, 12499.0]] Tr-S 2.3267501215362176 0.1529399573147963 [[3, 34], [484.0, 12763.0]] Tr-P 3.3313937378775282 5.0502968135993425e-18 [[86, 774], [401.0, 12023.0]] Tr-I 1.650337837837838 0.27863458597969176 [[6, 96], [481.0, 12701.0]] Hc-P 0.0 3.878707335324402e-05 [[0, 282], [487.0, 12515.0]] Hc-H 3.2274061329824915 0.025851679569557282 [[5, 41], [482.0, 12756.0]] NS 10.751131080856716 1.305277270354719e-89 [[170, 608], [317.0, 12189.0]]
<ipython-input-30-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 1.3440313171495968 3.604369122076473e-05 [[395, 5022], [455.0, 7775.0]] Pr-W 1.2536398164700666 0.005654178433248405 [[232, 2949], [618.0, 9848.0]] Pr-B 0.5594477210077569 0.0013943771003380573 [[29, 760], [821.0, 12037.0]] Pr-F 0.7480665425044982 0.056486502754447714 [[48, 948], [802.0, 11849.0]] En-Sd 1.1368928141308712 0.7802410711597803 [[4, 53], [846.0, 12744.0]] En-Sp 1.0738082954207901 0.7293927507435715 [[22, 309], [828.0, 12488.0]] En-W 2.514561196379378 0.13925818290763703 [[3, 18], [847.0, 12779.0]] En-Pd 0.11047419622213497 0.0034730143990739084 [[1, 135], [849.0, 12662.0]] En-Pp 1.062487350329094 0.7259017360143752 [[21, 298], [829.0, 12499.0]] Tr-S 0.44214646989537865 0.723224035443877 [[1, 34], [849.0, 12763.0]] Tr-P 0.43201041090583003 1.456756718809205e-05 [[23, 774], [827.0, 12023.0]] Tr-I 0.9405361374407583 1.0 [[6, 96], [844.0, 12701.0]] Hc-P 0.6355050017772813 0.14231796209014028 [[12, 282], [838.0, 12515.0]] Hc-H 0.0 0.18059138170311226 [[0, 41], [850.0, 12756.0]] NS 0.2628422777742927 9.257798595888108e-08 [[11, 608], [839.0, 12189.0]]
<ipython-input-30-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(order, rotation=90, ha="center")
Pr-A 0.20330767864992197 1.0617237344504706e-129 [[200, 5022], [1523.0, 7775.0]] Pr-W 0.5456743940254641 9.813622295675812e-19 [[242, 2949], [1481.0, 9848.0]] Pr-B 1.153838402044963 0.16161166533770505 [[117, 760], [1606.0, 12037.0]] Pr-F 1.7820114103123705 7.2028878087914934e-12 [[215, 948], [1508.0, 11849.0]] En-Sd 2.9668093031505665 9.497220997373584e-05 [[21, 53], [1702.0, 12744.0]] En-Sp 0.9359592279131979 0.8011079714191951 [[39, 309], [1684.0, 12488.0]] En-W 1.651994053390214 0.3223235794093011 [[4, 18], [1719.0, 12779.0]] En-Pd 3.677031675825403 7.389462668957816e-15 [[65, 135], [1658.0, 12662.0]] En-Pp 1.670630597060265 0.0003903880197203044 [[66, 298], [1657.0, 12499.0]] Tr-S 1.9710858672523852 0.09165174085684422 [[9, 34], [1714.0, 12763.0]] Tr-P 3.104554893365867 2.948513230104375e-45 [[287, 774], [1436.0, 12023.0]] Tr-I 1.8688934667451442 0.009904990297898413 [[24, 96], [1699.0, 12701.0]] Hc-P 1.1901516496335556 0.2986528581704706 [[45, 282], [1678.0, 12515.0]] Hc-H 5.700224874589171 2.5880502744480172e-11 [[31, 41], [1692.0, 12756.0]] NS 4.27778331171238 2.433247560310803e-70 [[303, 608], [1420.0, 12189.0]]
<ipython-input-30-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(order, rotation=90, ha="center")
df_bg = df_all.copy() # Make a copy so we don't override the other one
# Also have a look at how the marks plot over genes that are not significant
chrom_dir = os.path.join(supp_dir, "annot")
chromhmm_annot = pd.read_csv(f'{chrom_dir}/e16.5_forebrain_15_segments.csv')
print(len(chromhmm_annot), len(df_bg))
# Make sure we only keep 1 annotation per gene
chromhmm_annot = chromhmm_annot.groupby('external_gene_name').first()
# Merge this with our dataframe
df_bg = df_bg.merge(chromhmm_annot, on='external_gene_name', how='left', suffixes=('', '_chmm'))
# Ensure the new length is the same as the old length
run_annot_plot(df_bg, df, title=f'Training vs All')
53254 20900 Pr-A 1.622932800129698 2.818017067299491e-92 [[5022, 5950], [7775.0, 14950.0]] Pr-W 1.4846246759177353 7.04819175454217e-45 [[2949, 3508], [9848.0, 17392.0]] Pr-B 1.4129216422369582 1.3014535827248074e-11 [[760, 894], [12037.0, 20006.0]] Pr-F 1.4373626757386773 3.833534384602958e-15 [[948, 1102], [11849.0, 19798.0]] En-Sd 1.2740666888224217 0.18993047392586293 [[53, 68], [12744.0, 20832.0]] En-Sp 1.4157700584761927 1.0955105546292346e-05 [[309, 359], [12488.0, 20541.0]] En-W 0.8397035314634501 0.5748447551175753 [[18, 35], [12779.0, 20865.0]] En-Pd 1.1034986574000947 0.39619131465404356 [[135, 200], [12662.0, 20700.0]] En-Pp 1.2738035709523428 0.0021298162746898365 [[298, 384], [12499.0, 20516.0]] Tr-S 0.7927155505311111 0.3115949752781515 [[34, 70], [12763.0, 20830.0]] Tr-P 0.6793885517103826 8.200875290916301e-19 [[774, 1809], [12023.0, 19091.0]] Tr-I 1.2562160459806315 0.09547730174599986 [[96, 125], [12701.0, 20775.0]] Hc-P 1.143157320126739 0.09501093517145413 [[282, 404], [12515.0, 20496.0]] Hc-H 0.267657724638121 4.731558690297586e-19 [[41, 248], [12756.0, 20652.0]] NS 0.16188287235203244 0.0 [[608, 4923], [12189.0, 15977.0]]
<ipython-input-30-85741dc630f1>:75: UserWarning: FixedFormatter should only be used together with FixedLocator ax.set_xticklabels(order, rotation=90, ha="center")
Using the information from: https://epifactors.autosome.ru/ (EpiFactors: a comprehensive database of human epigenetic factors and complexes, by Yulia A. Medvedeva et al) we test each cluser for enrichment for epigenetic complex's and functions.
epi_genes = pd.read_csv(os.path.join(supp_dir, 'EpiGenes_main.csv'))
df_epi = df.merge(epi_genes, left_on='external_gene_name', right_on='MGI_symbol',
how='left', suffixes=('', '_eg'))
ax = sns.countplot(x="Function", data=df_epi)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
save_fig(f'Epigenes-function')
plt.show()
ax = sns.countplot(x="Target", data=df_epi)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
save_fig(f'Epigenes-target')
plt.show()
for set_label in vae_set_labels:
try:
vae_mse.u.dp([set_label])
ax = sns.countplot(x="Function", data=df_epi[df_epi[set_label] == True])
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
save_fig(f'Epigenes-function-{set_label}')
plt.show()
ax = sns.countplot(x="Target", data=df_epi[df_epi[set_label] == True])
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
save_fig(f'Epigenes-target-{set_label}')
plt.show()
# Complex_name Modification
ax = sns.countplot(x="Complex_name", data=df_epi[df_epi[set_label] == True])
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
save_fig(f'Epigenes-Complex_name-{set_label}')
plt.show()
ax = sns.countplot(x="Modification", data=df_epi[df_epi[set_label] == True])
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
save_fig(f'Epigenes-Modification-{set_label}')
plt.show()
except:
print(set_label)
<ipython-input-32-4ffd89933e6f>:7: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
-------------------------------------------------------------------------------- Set 1 --------------------------------------------------------------------------------
<ipython-input-32-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:37: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
-------------------------------------------------------------------------------- Set 2 --------------------------------------------------------------------------------
<ipython-input-32-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:32: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:37: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
-------------------------------------------------------------------------------- Set 3 --------------------------------------------------------------------------------
<ipython-input-32-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:32: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:37: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
-------------------------------------------------------------------------------- Set 4 --------------------------------------------------------------------------------
<ipython-input-32-4ffd89933e6f>:37: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
-------------------------------------------------------------------------------- Set 5 --------------------------------------------------------------------------------
<ipython-input-32-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:32: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:37: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
-------------------------------------------------------------------------------- Set 6 --------------------------------------------------------------------------------
<ipython-input-32-4ffd89933e6f>:21: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:32: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
<ipython-input-32-4ffd89933e6f>:37: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. plt.tight_layout()
###############################################################################
# #
# This program is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation, either version 3 of the License, or #
# (at your option) any later version. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program. If not, see <http://www.gnu.org/licenses/>. #
# #
###############################################################################
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sciviso import Vis
class Violinplot(Vis):
def __init__(self, df: pd.DataFrame, x: object, y: object, title='', xlabel='', ylabel='', hue=None, order=None,
hue_order=None, showfliers=False, add_dots=False, figsize=(1.5, 1.5), title_font_size=8,
add_stats=False, stat_method='Mann-Whitney',
label_font_size=6, title_font_weight=700):
super().__init__(df, figsize=figsize, title_font_size=title_font_size, label_font_size=label_font_size,
title_font_weight=title_font_weight)
self.df = df
self.x = x
self.y = y
self.xlabel = xlabel
self.ylabel = ylabel
self.title = title
self.hue = hue
self.order = order
self.hue_order = hue_order
self.showfliers = showfliers
self.add_dots = add_dots
self.add_stats = add_stats
self.stat_method = stat_method
def plot(self):
x, y, hue, order, hue_order = self.x, self.y, self.hue, self.order, self.hue_order
if not isinstance(self.x, str) and not isinstance(self.y, str):
vis_df = pd.DataFrame()
vis_df['x'] = x
vis_df['y'] = y
x = 'x'
y = 'y'
if self.hue is not None:
vis_df['colour'] = self.hue
hue = 'colour'
if order is None:
order = list(set(vis_df['x'].values))
order.sort()
else:
vis_df = self.df
# set the orders
if hue_order is None and hue is not None:
hue_order = list(set(vis_df[hue].values))
hue_order.sort()
if order is None:
order = list(set(vis_df[x].values))
order.sort()
ax = sns.violinplot(data=vis_df, x=x, y=y, hue=hue, hue_order=hue_order, order=order, palette=self.palette,
showfliers=self.showfliers, inner="quartile")
if self.add_dots:
ax = sns.stripplot(data=vis_df, x=x, y=y, hue_order=hue_order, order=order, color='.2')
if self.add_stats:
# Add all pairs in the order if the box pairs is none
pairs = []
box_pairs = []
for i in order:
for j in order:
if i != j:
# Ensure we don't get duplicates
pair = f'{i}{j}' if i < j else f'{j}{i}'
if pair not in pairs:
box_pairs.append((i, j))
pairs.append(pair)
# Add stats annotation
add_stat_annotation(ax, data=vis_df, x=x, y=y, order=order,
box_pairs=box_pairs,
test=self.stat_method, text_format='star', loc='inside', verbose=2)
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')
ax.tick_params(labelsize=self.label_font_size)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=self.label_font_size)
self.add_labels()
self.set_ax_params(ax)
return ax
# Have a look at the TFs and their targets for regulons (i.e. does grp2 have TFs that tagret group3?)
regs = pd.read_csv(supp_dir + 'regulons_mm10_A-B-C-D.csv')
reg_tfs = set(regs['tf'].values)
group_labels = {1: 'Prolifferation', 2: 'Posterior', 3: 'Immune response', 4: 'Development', 5: 'Forebrain'}
for i in range(1, n_clusters):
genes = df[gene_name].values[vae_set_idxs[i]]
# Flag the TFs in this group, we'll then test these to see if we have lots of the targets in another group
tfs = list(reg_tfs & set(genes))
# Now we want to go through each of the other groups and record how many were targetted
u.dp([group_labels[i], f'\n{len(tfs)} Tfs: ', ', '.join(tfs)])
for j in range(1, n_clusters):
genes_targets = df[gene_name].values[vae_set_idxs[j]]
total_targets = 0
for tf in tfs:
targets = set(regs[regs['tf'] == tf]['target'].values)
total_targets += len(list(targets & set(genes_targets)))
if len(list(targets & set(genes_targets))) > 0:
print(f'{tf} -->', ', '.join(list(targets & set(genes_targets))))
print(f'--------------- Total for {group_labels[i]} targeting {group_labels[j]}: {total_targets} -------------')
#regs = pd.read_csv(supp_dir + 'regulons_mm10_A-B-C-D.csv')
regs[regs['tf'] == 'Cdkn1b'] #Tbx3, mesoderm markeds Hand1, gata2
#df_sig = pd.read_csv(f'{input_dir}df-significant_epi-2500_20210124.csv')
for g in regs[regs['target'] == 'Foxm1']['tf'].values:
if len(df_training[df_training[gene_name] == g]) > 0:
print(g, '\n')
#print(df_sig[df_sig[gene_name] == g])
fox_df = df_training[df_training[gene_name] == 'Rb1']
h3k36 = [c for c in fox_df.columns if 'H3K27me3' in c and 'brain' in c and 'signal' in c and 'median' not in c]
for c in h3k36:
print(c, fox_df[c].values)
-------------------------------------------------------------------------------- Prolifferation 7 Tfs: Nfia, Pbx3, Bcl11a, Tcf25, Dnmt3a, Smarcc2, Ncoa1 -------------------------------------------------------------------------------- Pbx3 --> Plcb1, Macf1, Apbb2, Nbea, Pde4d, Fyn, Map4, Akap6, Prickle2, Runx1t1, Daam1, Kcnma1 Bcl11a --> Cntnap2, Prkcb Tcf25 --> Celsr3, Ppp2r1a Smarcc2 --> Myo18a, Clstn1 --------------- Total for Prolifferation targeting Prolifferation: 18 ------------- Pbx3 --> Macf1, Cacna2d1, Tns1, Tiam2 Bcl11a --> Ephb1, Prkcb, Pax5, Pde1c, Elmo1, Nfic, Cacna1e Tcf25 --> Celsr3 Dnmt3a --> Sfrp5, Sfrp1, Igfbp3 Smarcc2 --> Ahnak, Epb41, Clstn1, Stat6 --------------- Total for Prolifferation targeting Posterior: 19 ------------- Bcl11a --> Sp100 --------------- Total for Prolifferation targeting Immune response: 1 ------------- Nfia --> Fn1, Trp53, Slc25a5 Pbx3 --> Flna Bcl11a --> Gdi2 Tcf25 --> Pcbp2, Sf3b2, Hnrnpul1, Hnrnpl Dnmt3a --> Sfrp1 Smarcc2 --> Ankrd11, Ahnak, Epb41 Ncoa1 --> Hipk1, Ubr5 --------------- Total for Prolifferation targeting Development: 15 ------------- Nfia --> Gfap, S100b Pbx3 --> Kcnma1 Bcl11a --> Sp100, Runx3, Ampd3, Obscn --------------- Total for Prolifferation targeting Forebrain: 7 ------------- -------------------------------------------------------------------------------- Posterior 86 Tfs: Gata4, Pou2f2, Rreb1, Creb5, Tfap2c, Otx2, Pitx1, Mafb, Sp2, Klf4, Foxl2, Junb, Rarg, Twist1, Bhlhe22, Fosl2, Prox1, Maf, Twist2, Onecut1, Esrra, Tal1, Hoxa11, Msc, Neurod1, Zbtb7a, Fli1, Hoxa10, Sox6, Stat6, Zhx2, Lhx4, Cux1, Wt1, Hic1, Lhx3, Ebf1, Nr2f2, Irf8, Ets1, Lyl1, Cebpb, Meis1, Hoxb13, Epas1, Hoxb7, Pax8, Tfap2a, Dnmt3a, Snai1, Pou4f2, Sox10, Zfx, Nfic, Klf2, Six2, Nkx3-1, Arntl, Nr5a1, Gata6, Gata3, Hes1, Creb3l1, Hoxa1, Satb1, Cebpa, Foxa2, Msx2, Tcf7, Pax6, Nfatc2, Gli1, Gata2, Hoxa9, Foxa1, Ppara, Erg, Trp73, E2f6, Pax3, Pgr, Usf1, Foxo1, Nfatc1, Lhx2, Pax5 -------------------------------------------------------------------------------- Pou2f2 --> Hivep3, Inpp4a, Prkcb, Dpysl2, Rabgap1l, Dst, Anks1b, Nedd4l, Scaper, App Otx2 --> Macf1, Nbea, Dclk1 Mafb --> Pcsk2, Rtn1, Mtss1, Dst, Apbb2, Cacna1d, Dtna, Spag9, Tenm2, Mmp9, Trio, Daam1 Sp2 --> Mmp9 Klf4 --> Pfkp Foxl2 --> Ppargc1a Junb --> Kalrn, Cmip, Clu, Rtn4, Vopp1, Kcnma1, Sh3bp5 Rarg --> Cdh13, Ncoa1 Fosl2 --> Clu Prox1 --> Grin2b, Nedd4l Maf --> Tecr, Pde4d, Sh3bp5 Onecut1 --> Eif4g3, Olfm3, Macf1, Rapgef4, Unc13c, Csmd3, Nrxn1, Arpp21 Esrra --> Nfia, Dlg2, Hdac11, Atp1b1, Hk1, Slc22a23, Myo18a, Nrxn1, Rap1gap Tal1 --> Cdh13, Ppp3ca Neurod1 --> Scrt1, Ctsb, Rtn4 Zbtb7a --> Actb, Srcin1, Pfkp, Fbxl16, Palm, Dnmt3a, Slc22a23, Thra, Ndrg2, Bcr, Agrn Sox6 --> Nfasc, Dnajc6 Zhx2 --> Actb, Madd, Srcin1, Vat1, Spag9, Ctsb, Hivep2, Cplx2, Mcf2l, Dnmt3a, Acvr2a, Prkar1a, Zbtb4, Stmn3, Sptan1, Sptbn1, Osbp2, Hsp90ab1, Pik3r1, Lzts3, Tcf25, Agap1, Mfsd6, Ttyh3, Stx1b, Sqstm1, Nrsn2, Gng4, Slc25a12, Garnl3 Cux1 --> Sptbn1, Srcin1, Eef2, Macf1, Sqstm1, Tex2, Ahi1, Daam1 Hic1 --> Lrp8, Vldlr Ebf1 --> Inpp4a, Sptbn1, Slc8a1, Dpysl2, Ptprs, Coro2b, Fyn, Cmip, Ncald, Spock1 Ets1 --> Tnc, Ttyh3, Ctsb, Mmp9, Cdh13, Nrp1, Mgat5b Lyl1 --> Hivep3, Plxna2, Clip2, Pld3, Cbfa2t3, Gng2 Meis1 --> Sptbn1, Slc8a1, Lsamp, Nfia, Nfib, Dst, Dpysl3, Ryr3, Tnik, Daam1 Hoxb13 --> Map1b, Thsd7a, Nfia, Dlg2, Psd3, Pde4d, Rit2, Sgip1, Nrxn1, Ryr2, Trio Hoxb7 --> Shank2, Cntnap2, Slc22a23, Gng4, Map2 Pax8 --> Ncam1 Tfap2a --> Prkca, L1cam, Ache, Snap91, Timp2, App Snai1 --> Cxadr Pou4f2 --> Rabgap1l, Dst, Cntn5, Ctnnd2, Nlgn1, Myt1l, Ncoa1 Nfic --> Fabp7 Six2 --> Dclk1, Cpne4, Mtss1, Ctnna2, Mycbp2, Cntn5, Nedd4l, Nrxn1, Rims2 Arntl --> Nr1d1 Gata6 --> Pde4d, Dpysl2, Sema6a Gata3 --> Slc24a3, Apba1, Nrcam, Blcap, Grm7, Arnt2 Hes1 --> Gabra3, Gdap1, Nfia, Cntnap2, Frmd3, Adarb1, Adam22, AW551984, Agtpbp1, Prrc2b, St6galnac5, Fam131b, Lrrc55, Ctnnd2, Caln1, Pten, Celf4, Ptgds, Negr1, Ppfia2, Tmx4, Grm3, Map2 Creb3l1 --> Slc1a4, Nrsn2 Hoxa1 --> Ncam1 Satb1 --> Cntnap2, Spag9, Chodl, Kcnma1, Grid2, Dync1i1, Gabrb3, Pde4d, Csmd3, Cntn5, Cdh18, Slc9a7, Slc8a1, Stx1b, Tbc1d9, Cttnbp2, Kalrn, Unc13c, Drp2, Gdap1 Cebpa --> Eln, Crmp1, Timp2, Vldlr, Cdh13, Kcnma1 Foxa2 --> Ttr Tcf7 --> Phactr1, Camk4, Dst, Trio, Grm7, Rtn4 Pax6 --> Ctnnd2, Olfm3 Gli1 --> Tsc22d1, Kidins220, Mtss1 Gata2 --> AW551984, Dpysl2, Htr5a Hoxa9 --> Agtpbp1, Spock1 Foxa1 --> Ttr Ppara --> Mmp9, Acox1, Hmgcs2 Erg --> Mmp9 Trp73 --> Myo5a, Rapgef4, Mvb12b E2f6 --> Srcin1, Nfib, Macf1, Rap1gap, Agrn Pax3 --> Dpysl2 Usf1 --> Apc, Slc1a3, Ctsb, Ptgds Foxo1 --> Daam1, Cnr1 Nfatc1 --> Actb, Dst, Macf1, Fyn, Hivep2 Lhx2 --> Adgrl3 Pax5 --> Macf1, Apbb2, Bcl11a, Fcho1, Prkce --------------- Total for Posterior targeting Prolifferation: 302 ------------- Pou2f2 --> Prkcb, Dock9, Dst, Arhgef3, Dlgap4, Tmc6, Abr, Nedd4l, Ank1, Egln3, Ebf1, App Creb5 --> Ptger4, Dgkg, Tgfbr3, Bdnf Tfap2c --> Ret, Foxc1, Foxa1 Otx2 --> Macf1, Creb5, Itga1 Mafb --> Tjp2, Rtn1, Mtss1, Dst, Elmo1, Mmp9, Adamts2, Tns3, Cacna2d3 Sp2 --> Mmp9 Klf4 --> Cdh1, Ccnd1, Thbd Junb --> Egfr, Mical2, Zfp36l1, Tiam2, Smarca2 Rarg --> Thbd, Cyp26a1, Slc5a5, Smarca2, Foxc1, Icam1, Nkx2-1, Pthlh, Ccnd1 Twist1 --> Cdh1, Cldn7, Gli1 Bhlhe22 --> Adgra2, Aldh1a2, Aebp1 Fosl2 --> Ndrg1 Prox1 --> Add3, Tns3, Nrp2, Fzd10, Atp1a1, Nedd4l, Afap1l2 Maf --> Nol4l, Rgs10, Arhgef3, Il4ra, Ltbp3 Twist2 --> Icam1, Erbb3, Cdh1 Onecut1 --> Eif4g3, Macf1, Rapgef4, Ank3, Pde4b, Csmd3, Cacna2d1, Arpp21, Bnc2, Unc5d Esrra --> Mgll, Wnk2, Nfatc2, Llgl2, Atp1b1, Sox6, Rap1gap, Strbp, Syt12, Gsn Tal1 --> Aldh1a2, Dock9, Cd34, Chst11, Elmo1, Erg Hoxa11 --> Hoxa10 Neurod1 --> Scrt1, Asic4, Rgs10, Tbx1, Mical2, Arhgef19, Gabbr2, Tal1, Grm4, Mef2d Zbtb7a --> Srcin1, Hs6st1, Baiap3, Nacc2, Stat6, Pcdh1, Ahdc1, Atp2b2, Dnmt3a, Ndrg2, Ptch1, Cux1, Pdgfb, Ltbp3, Rnf220, Nfic, Arhgap23, Akt1, Lama5, Npas1, Piezo1, Abr, Plec, Fmnl1, Sigirr Fli1 --> Igfbp3, Erg, Tgfbr2 Hoxa10 --> Bhlhe22, Lyl1, Emx2, Wt1, Cdkn1c, Snai1 Sox6 --> Nfasc, Atp2b4, Ank3, Dnajc6 Zhx2 --> Vgll4, Jade2, B3gnt2, Srcin1, Kiss1r, Nol4l, Shh, Ctdsp1, Arpc1b, Foxa2, Lamb1, Prkcz, Cplx2, Tmem9, Tmc6, Klhl21, Tbx2, Prdm16, Sowaha, Ascl2, Epha2, Epas1, Dnmt3a, Phldb1, Efna1, Lsr, Icam1, Hsf4, Arhgap27, Cdc42ep1, Ccnd1, Arhgef16, Stmn3, Osbp2, Sptbn1, Sstr5, Vamp5, Tfeb, Rab11fip3, Gprc5c, Rnf220, Nfic, Zfp36l1, Erbb3, Zfp503, Egln3, Foxa1, Insm2, Zfp518a, Fkbp5, Ndrg1, Sphk1, Cbx4, Satb2, Epcam, Plec, Adra1d, Gnas, Gng4, Fgfr3, Synj2, Fzd5, Tns3, Garnl3, Adap1 Cux1 --> Racgap1, Sptbn1, Srcin1, Macf1, Itgb5, Itgb4, Nfic Wt1 --> Pdgfa, Ets1, Egfr, Igf2, Nr5a1, Cdh1, Pax2, Nphs1 Hic1 --> Epha2, Atoh1, Vldlr Ebf1 --> Sptbn1, Fli1, Ptprs, Nol4l, Rftn1, Rbm20, Cux1, Ncald, Ank1, Elmo1, Prkcd, Spock1, Adap1 Nr2f2 --> Ppara, Penk, Acta1 Ets1 --> Met, Fli1, Tnc, Jak3, Col1a1, Mgat5b, Pcsk6, Tfap2a, Mmp9, Pthlh, Ccnd1, Bmp4, Atp2a3, Tgfbr2, Wt1, Flt1, Foxd1, Erg, Ndrg1, Nrp1 Lyl1 --> Trpm2, Plcg2, Hlx, Cd34, Sh2b3, Plec, Elmo1, Tal1, Ptk2b, Tmc6, Erg, Dgkz Cebpb --> Adm, Igf2, Gadd45g Meis1 --> Vgll4, Sptbn1, Cacna1a, Ets1, Dst, Dpysl3, Phactr3, Tnik Hoxb13 --> Esrrg, Col14a1, Dock9, Dnah11, Ank3, Cntn4, Ryr2, Bnc2, Erg Epas1 --> Adora2a, Sphk1, Pdgfb, Flt1, Egln3 Hoxb7 --> Pdgfa, Megf11, Col12a1, Egfr, Mal2, Gng4, Mms19, Wnt5a, Ccnd1 Pax8 --> Ncam1, Gata3, Slc5a5, Wt1 Tfap2a --> Ache, Ets1, Egfr, Hoxa4, Foxa1, Snap91, Kit, Penk, Cdh1, Igfbp5, Adm, App Dnmt3a --> Sfrp5, Sfrp1, Igfbp3 Snai1 --> Cdh1, Cldn3, Ercc1 Pou4f2 --> Dst, Pde4b, Ctnnd2, Nlgn1, Ccnd1 Sox10 --> Ret, Sox6, Pdgfra, Mbp Zfx --> Zbtb7a, Tead3, Pcgf5, Klhl21 Nfic --> Col18a1, Col1a1, Smug1, Tfap2a Klf2 --> Thbd Six2 --> Kank1, Col13a1, Dpp6, Six1, Dock9, Mtss1, Zbtb7c, Prkch, Thsd4, Nedd4l, Zfp423 Nkx3-1 --> Six6, Ccnd1 Gata6 --> Prdm16, Cux1, Barx2, Mical2, Sox6, Zfp36l1, Sptb, Ptk2b Gata3 --> Sst, Grm7, Tgfbr3, Foxc1, Egfr, Il4ra, Maf, Cdh1, Epor, Erg Hes1 --> Cacna1a, Prune2, Col2a1, Cdkn2b, Zc3hav1, Gpc5, Sptb, Prkg1, Plekha2, Htr1a, Chst8, Gli1, Pcdh15, Gfod1, Smarca2, Add3, Cul9, Aldh1a2, Lamc2, Npr3, Corin, Etfa, Ctnnd2, Pdzrn4, Celf4, Cpm, Rassf5, Atp1a1, Mcidas Creb3l1 --> Kcnk1, Plec Hoxa1 --> Lyl1, Epas1, Prph, Ncam1, Ccnd1 Satb1 --> Vps13a, Aldh1a3, Inhbb, Slc22a4, Tcf7, Asic2, Trank1, Tmem9, Pax9, Gabra5, Ephb1, Capn1, Otx2, Clstn2, Slc16a9, Gli1, Ank1, Grm4, Cobl, Gsn, Hspb1, Csmd3, Dgki, Rnf220, Col27a1, Plcg2, Cdh11, Slc27a1, Gata3, Ptpra, Nmnat2, Susd1, Tns3, Ptk2b Cebpa --> Mmp11, Ace, Igf2, Crmp1, Nr5a1, Aebp1, Tsc22d3, Icam1, Foxa2, Vldlr, Ccnd1 Foxa2 --> Cdh1 Msx2 --> Nkx3-1 Tcf7 --> Kcnk1, Tgfbr3, Dst, Ccnd2, Nkd1, Plec, Sox6, Grm7, Ccnd1 Pax6 --> Met, Ctnnd2 Gli1 --> Sfrp1, Mtss1, Pdgfra, Pde4b, Foxa2, Asic2, Kank1, Ccnd2, Wnt9a, Wnt2b, Ptch1, Bmp4, Npdc1, Wnt4, Pltp, Prkch, Wnt10b, Akt1, Foxa1, Ntrk2, Bhlhe41, Mtus1, Hhip, Wnt5a Gata2 --> Lyl1, Elmo1, Wt1 Hoxa9 --> Nemp1, Zfp503, Spock1 Foxa1 --> Ret, Foxc1, Erbb3, Cebpa Ppara --> Fabp3, Cpt1a, Ccnd1, Mmp9 Erg --> Icam1, Mmp9 Trp73 --> Sbf2, Megf6, Pmaip1, Rapgef4, Lgr6, Dgkz E2f6 --> Srcin1, St3gal1, Nol4l, Ptpru, Macf1, Ctdsp1, Nacc2, Ptp4a2, Nfatc2, Icam5, Pfkfb3, Bahd1, Pde4a, Ltbp3, Rap1gap, Kazald1, Efna3, Piezo1, Abr, Nfatc1, Gnas, Fzd5 Pax3 --> Met, Ets1, Wt1, Shh Pgr --> Ccnd1 Usf1 --> Trim25, Hoxb7, Cebpa, Tgfb1, Hoxb4, Slc1a3 Foxo1 --> Klf2, Ccnd2, Bcl2l11, Timp3, Cdkn2b, Cdkn1c, Cnr1, Ccnd1 Nfatc1 --> Phf19, Dst, Macf1, Icam1, Creb5, Smarca2, Ptk2b Lhx2 --> Sox6, Ccnd1 Pax5 --> Abr, Macf1, Tcf7, Irf8 --------------- Total for Posterior targeting Posterior: 576 ------------- Pou2f2 --> Lrmp, Il21r, Rhoh, Vwa3b, Tbxas1 Creb5 --> Ly96 Otx2 --> Arhgap15, Diaph3 Mafb --> Tbxas1 Foxl2 --> Ccl3, Bcl2a1b Rarg --> S100a8 Twist1 --> Timp1 Maf --> Pstpip1 Onecut1 --> Mlip, Diaph3 Msc --> Cdc6 Neurod1 --> Sh2d6 Fli1 --> Gp9 Sox6 --> Nek5 Zhx2 --> Ppp1r3g Irf8 --> Tlr4 Ets1 --> Gp9, Timp1, Was, Rad51, Tbxas1 Lyl1 --> Sash3, Cacna2d4 Meis1 --> Cfap61 Hoxb7 --> Zpld1, Oc90 Pax8 --> Tshr Tfap2a --> Cyp11a1 Snai1 --> Ccl2 Pou4f2 --> Arhgap15 Sox10 --> Gjc2 Nfic --> Pole2 Klf2 --> Ccr5 Nkx3-1 --> Actg2 Nr5a1 --> Cyp11a1 Gata3 --> Rhoh Hes1 --> Cdhr3, Aoah, Clec5a, Siglech, Il20rb Satb1 --> Shmt1, Actg2, Clec5a, Diaph3, Spag16 Cebpa --> Gucy2c, Itgal Msx2 --> Abcg3 Tcf7 --> Sp100, Arhgap15, Rhoh Gli1 --> Myo15 Hoxa9 --> Spag17, Arhgap15 Trp73 --> Plac8 Foxo1 --> Tnfsf10 Nfatc1 --> Gpr183 Pax5 --> Sp100, Rhoh, Rad51b --------------- Total for Posterior targeting Immune response: 67 ------------- Pou2f2 --> Smad3 Creb5 --> Rdx, Zfp516 Tfap2c --> Foxc1, Crabp2 Mafb --> Tjp2, Sox9, Tns3 Sp2 --> Hnrnpul1 Klf4 --> Ccnd1, Birc5 Junb --> Zfp36l2, Zfp36l1, Wwtr1 Rarg --> Mif, Foxc1, Crabp2, Khsrp, Ccnd1 Twist1 --> Gli1 Fosl2 --> Brca1 Prox1 --> Tns3 Maf --> Smad3 Twist2 --> Fn1, Cdh2, Itgb1 Onecut1 --> Ank3, Eif3e, Sox2 Esrra --> Txnrd1, Ephb4 Msc --> Cdc25a, Cdc6, Mcm7 Zbtb7a --> Actb, Cux1, Pcbp1, Ptch1, Notch1, Plec, Ptbp1, Rnf220, Igf2bp1, Gatad2a, Khsrp Hoxa10 --> Trp53 Sox6 --> Trp53, Ank3, Smad3 Stat6 --> Rhoa Zhx2 --> Actb, Ccna2, Srrm1, Hnrnpl, Hnrnpf, Ncl, Lamb1, Ptbp1, Ajuba, Bzw1, Igf2bp1, Actg1, Acin1, Slc16a3, Arid1a, Txnrd1, Pcbp1, Hspa8, Abl1, Ywhaz, Gatad2a, Srsf6, Rtkn, Acly, Ccnd1, Anp32b, Tubb4b, Hsp90ab1, Ube2d3, Brd2, Peg12, Rnf220, Ptma, Zfp36l1, Ankrd11, Midn, Zfp518a, Plec, Gnas, Arhgef17, Myh10, Dnmt1, Fn1, Tns3, Srsf5 Cux1 --> Racgap1, Eef2, Slc16a3, Ect2, Hspd1, Puf60 Wt1 --> Odc1, Igf2, Ube2i, Smad3, Thbs1 Hic1 --> Ackr3 Ebf1 --> Tcf3, Cux1, Ackr3, Arhgef17 Ets1 --> Thbs1, Pold1, Parp1, Itga5, Cdc37, Cdk4, Ccnd1, Trp53, Col1a1, Foxd1, Rad51 Lyl1 --> Ccnd3, Plec Cebpb --> Igf2, Top1 Meis1 --> Dock5 Hoxb13 --> Map1b, Ank3 Hoxb7 --> Otx1, Ccnd1, Gli3 Pax8 --> Trp53 Tfap2a --> Crabp2, Taf7, Igfbp5, Ccnb1 Dnmt3a --> Sfrp1 Snai1 --> Fn1, Cdh2, Thbs1 Pou4f2 --> Ccnd1 Sox10 --> Pdgfra Zfx --> Nsd2, Hnrnpf, Pum2, Ube2d3, Setd5, Ddx6, Mcm7, Celf1, Anp32b Nfic --> Trp53, Col1a1, Itga5, Col18a1 Nkx3-1 --> Ccnd1 Gata6 --> Zfp36l1, Cux1 Gata3 --> Foxc1, Epha7, Tcf7l2, Fn1 Hes1 --> Nop58, Ipo5, Prrc2b, Col2a1, Etfa, Ncl, Kif18b, Gli1, Dbf4 Creb3l1 --> Arid1a, Serpinh1, Plec Hoxa1 --> Pcna, Ccnd1 Satb1 --> Cenpe, Polr2m, Hdac1, Cdh11, Mki67, Sae1, Scube1, Mcm5, Ncapd2, Cdk4, Gli1, Tpm3, Rnf220, Numa1, Tns3, Psme4 Cebpa --> Scd2, Fasn, Cdk2, Top2a, Hspa4, Igf2, Uhrf1, Crmp1, Ccnb1, Pcna, Fn1, Srebf2, Ccnd1 Foxa2 --> Tcf7l2 Msx2 --> Cdh2 Tcf7 --> Ccnd2, Plec, Birc5, Tcf3, Ccnd1 Pax6 --> Itgb1, Itga5 Nfatc2 --> Cdk4 Gli1 --> Sfrp1, Npdc1, Ccnd2, Sox9, Pdgfra, Gli2, Cdk2, Trp53, Ptch1 Hoxa9 --> Vim Foxa1 --> Foxc1, Brca1 Ppara --> Hmgcs1, Ccnd1 Erg --> Vim Trp73 --> Trp53, Cdk1, Sbf2, Megf6 E2f6 --> Ptp4a2, Slc16a3, Tacc3, Txnrd1, Pcbp1, Ptprf, Tcf3, Gnas, Smad3, Ptma, Brca1 Pgr --> Ccnd1 Usf1 --> Fasn, Nup188, Ccnb1, Cdk4, Tyms, Sf1, Serbp1 Foxo1 --> Ccnd2, Ccnb1, Mcm5, Ccnd1, Ccnb2 Nfatc1 --> Actb, Ubr5 Lhx2 --> Ccnd1 Pax5 --> Lonp1, Trp53, Smarca4, Smad3 --------------- Total for Posterior targeting Development: 266 ------------- Gata4 --> Nppa Pou2f2 --> Hivep3, Ciita, Nlrc5, Il21r, Rhoh, Runx3, Anks1b, Spp1, Ipcef1, Kcnn3, Vwa3b, Tbxas1 Creb5 --> Lgals3bp, Ly96, Dgkg Otx2 --> Trpc4, Arhgap15, Pde1a Mafb --> Tbxas1, Pcsk2, Cacna1d, Mmp9 Sp2 --> Nrl, Mmp9 Klf4 --> Lama3 Foxl2 --> Nr5a2, Bcl2a1b, Fos, Ccl3, Ppargc1a, Il11 Junb --> Fhad1, Ampd3, Kcnma1 Rarg --> Folr2, S100a8 Twist1 --> Fos Bhlhe22 --> Kcnn3 Fosl2 --> Bcl6 Prox1 --> Grin2b, Nr5a2 Maf --> Itgb7, Pstpip1 Twist2 --> Fos, Runx2, Gdf15 Onecut1 --> Olfm3, Rapgef4, Unc13c, Csmd3, Aldh1a7, Chst9, Arpp21, Mlip, Aldh1a1 Esrra --> Rgs6, Dlg2 Tal1 --> Cdh23 Msc --> Lta Neurod1 --> Cdh23, Sh2d6 Zbtb7a --> Eps8l2, Fmnl1 Fli1 --> Fos, Gp9 Hoxa10 --> Il11, Wt1 Sox6 --> Pde11a, Nek5 Stat6 --> Fos Zhx2 --> Serpinb1b, Kiss1r, Hnf1b, Comtd1, Rph3al, Gdf15, Bcl6, Serpinb1a, Nrl, Abcc3, Ascl2, Rhof, Madcam1, L3mbtl1, Ampd3, Rora, Gper1, Osbp2, Sstr5, Fstl3, Trim47, Mfsd6, Exoc3l4, Khk, Parp10, Trim65, Nrsn2, Gng4, Acyp2, Serpinb6b Cux1 --> Aloxe3 Hic1 --> Fgfbp1 Ebf1 --> Rgs6, Dennd3, Cdh23 Nr2f2 --> Apoa1, Myh6 Irf8 --> Cybb, Tlr4, Ciita Ets1 --> Fas, Cd53, Grpr, Fos, Gp9, Fgfbp1, Csf3r, Wt1, Mmp9, Oas2, Tbxas1 Lyl1 --> Hivep3, Trpm2, Cd53, Vav1, Sash3, Far2, Cybb, Arhgap22, Cacna2d4 Cebpb --> Fos, Csf3r Meis1 --> Ryr3, Cfap61, Pde11a, Lsamp Hoxb13 --> Thrb, Dlg2, Rit2, Cntn4, Ryr2 Epas1 --> Car9 Hoxb7 --> Frmpd3, Zpld1, Kcnip3, Rhof, Baalc, Mal2, Gng4 Pax8 --> Tshr, Wt1 Tfap2a --> Fas, Hgf, Cftr, Cyp11a1, Dbh, S100b Snai1 --> Fos, Ccl2 Pou4f2 --> Inpp4b, Kcnip4, Cntn5, Nlgn1, Arhgap15 Sox10 --> Gjc2, Mpz, Mag Nfic --> Matn1, Gfap Klf2 --> Ccr5, Vav1 Six2 --> Cntn5, Cpne4 Nkx3-1 --> Fos, Actg2 Nr5a1 --> Cyp11a1 Gata6 --> Clic5, Nr5a2, Lmcd1, Apoa1, Barx2, Pkib Gata3 --> Grm7, Rhoh, Tlr5, Spef2, Neto1 Hes1 --> Gabra3, Cdhr3, C1qtnf3, Aoah, Grm8, Frmd3, Efcab10, Cd33, Htr1a, Cdkl4, Clec5a, Slitrk4, Pcdh15, Sntg1, St6galnac5, Lrrc55, Caln1, Siglech, Ptgds, Fgf1, Ppfia2, Rassf5, Sntg2, Grm3, Kcnt2 Creb3l1 --> Nrsn2 Hoxa1 --> Ren1 Satb1 --> Cfap54, Slc22a4, Chodl, Pcsk1, Kcnma1, Gabra5, Jsrp1, Slc16a9, Cdkl4, Grid2, Clec5a, Dync1i1, Tekt5, Clic5, Lrp1b, Ankrd34c, Csmd3, Cntn5, Hipk4, Cdh18, Spag16, Slc9a7, Sgcz, Dnah6, Samd12, Dnali1, Unc13c, Cybb, Inhba, Oprk1, Actg2, Cyp4f13 Cebpa --> Fas, Eln, Kcnh8, Cftr, Gucy2c, Apoa1, Csf3r, Lbp, Spp1, Kcnma1, Msr1 Foxa2 --> Ttr, Apoa1, Cftr Msx2 --> Abcg3, Spp1 Tcf7 --> Sp100, Cd86, Rgs6, Rhoh, Skap1, Qpct, Camk4, Arhgap15, Grm7 Pax6 --> Olfm3, Pcsk1 Nfatc2 --> Egr3 Gli1 --> Fas, Tnfrsf23, Itih3, Igfbp6, Myo15, Wnt10b, Bhlhe41, Spp1 Gata2 --> Cybb, Psd4, Htr5a, Wt1 Hoxa9 --> Spag17, Ipcef1, Gsap, Arhgap15, Rgs13 Foxa1 --> Ttr, Apoa1, Cftr Ppara --> Hmgcs2, Apoa1, Cd36, Mmp9, Apobr Erg --> Mmp9 Trp73 --> Lgr6, Rapgef4, Plac8 E2f6 --> Kazald1, Efna3 Pax3 --> Wt1 Usf1 --> Ptgds, Slc22a2, Isg20, Ciita Foxo1 --> Pdk4, Trim63, Tnfsf10, Rag1 Nfatc1 --> Egr3 Pax5 --> Gper1, Zc3h12d, Sp100, Rhoh, Runx3 --------------- Total for Posterior targeting Forebrain: 321 ------------- -------------------------------------------------------------------------------- Immune response 2 Tfs: Stat4, Spdef -------------------------------------------------------------------------------- Spdef --> Sqstm1, Mmp9 --------------- Total for Immune response targeting Prolifferation: 2 ------------- Stat4 --> Ccnd2 Spdef --> Cdh1, Mmp9 --------------- Total for Immune response targeting Posterior: 3 ------------- --------------- Total for Immune response targeting Immune response: 0 ------------- Stat4 --> Ccnd2 --------------- Total for Immune response targeting Development: 1 ------------- Spdef --> Mmp9 --------------- Total for Immune response targeting Forebrain: 1 ------------- -------------------------------------------------------------------------------- Development 35 Tfs: Smarcc1, E2f7, Foxm1, Pax6, Srebf2, Mta2, Hmga1, Sox11, Pbx2, Arid1a, Ctcf, Gli2, Tgif2, Gli1, Tfdp1, Tcf3, Twist1, Cux1, Tcf7l2, Sox9, Id3, Tead2, Dnmt1, Trp53, Nono, Maz, Ybx1, Ssrp1, Usf1, Smad3, Gli3, Lhx2, Mybl2, Sox2, Nr2f2 -------------------------------------------------------------------------------- Smarcc1 --> Unc13a, Ppm1h, Actb, Rapgef4, Fcho1, Clip2, Timp2, Dnm1, Prkce, Bcr, Daam1, Stx1a, Sh3bp5, Zfhx3, Olfm1, Mcf2l, Cmip, Thra, Nrxn2, Osbp2, Pdxk, Trim2, Syn3, Tmcc2, Ttyh3, Myo18a, Tom1l2, Rap1gap, Atp2a2, Trio, Epb41l1, Garnl3, Plxna2, Camk2g, Soga1, Rtn4 E2f7 --> Dst, Agrn Pax6 --> Ctnnd2, Olfm3 Mta2 --> Bcr, Plxna2 Sox11 --> Dst, Gria2, Rapgef4, Map4k4, Ank2, Rims1, Nedd4l, Chodl, Pclo, Rims2, Erc1 Pbx2 --> Prkcb, Pik3r1, Fyn, Nfasc, Garnl3 Ctcf --> Tro, B4galnt1, Celsr3, App Gli1 --> Tsc22d1, Kidins220, Mtss1 Cux1 --> Sptbn1, Srcin1, Eef2, Macf1, Sqstm1, Tex2, Ahi1, Daam1 Id3 --> Actb, Dpysl2, Nfia, Mtss1, Macf1, Dnm1, Adarb1, Bcr, Znrf1, Prkar2b, Sh3bp5, Prkcb, Cmip, Hk1, Slc22a23, Adgrb2, Ahi1, Pacsin1, Pdxk, Rabgap1l, Sdc3, Tmcc2, Grip2, Mgat3, Rap1gap, Atp2a2, Mdga1, Epb41l1, Dnajc6, Fyn, Camk2g, Soga1, Rtn4 Tead2 --> Atp6v1a, Dnajb5 Dnmt1 --> Actb, Slc7a11, Reln, Gad1, Epm2aip1, Ptn Trp53 --> Hsp90ab1, Scn3b, Smpd3, Map4, Cdh13, Pten Maz --> Srcin1, Pdxk, Stk32c, Epb41l1, Nfia, Palm, Clip2, Agrn Ssrp1 --> Eef2, Sqstm1 Usf1 --> Apc, Slc1a3, Ctsb, Ptgds Smad3 --> Tnc, Mmp9 Gli3 --> Mtss1 Lhx2 --> Adgrl3 Sox2 --> Eif4g3, Phactr1, Cntnap2, Nav3, Hivep2, Nrxn1, Spag9, Syt1, Pclo, Ryr2, Nrxn3, Nbea, Sorcs1, Daam1, Plcb1, Cpne4, Pbx3, Magi2, Thsd7a, Dlgap1, Ryr3, Ntm, Lrrc7, Dtna, Slc22a23, Anks1b, Csmd1, Gria1, Shank2, Xpr1, Rabgap1l, Trim2, Ctnna2, Dclk2, Frmd4a, Erc1, Pde4d, Agap1, Robo2, Csmd3, Clasp2, Akap6, Ctnnd2, Nedd4l, Galntl6, Ntrk3, Cdh18, Trio, Hivep3, Map1b, Slc8a1, Grik2, Lsamp, Ptprd, Ppargc1a, Dlg2, Opcml, Kalrn, Ttc28, Dpp10, Plxna2, Nlgn1, Spock1, App --------------- Total for Development targeting Prolifferation: 202 ------------- Smarcc1 --> Vgll4, Cbx8, Plekha6, Dagla, Sh2b3, Plcd3, Card10, Sptb, Ptp4a2, Cdh22, Phldb1, Ank1, Nrxn2, Mmp28, Rarg, Celsr2, Mgll, Fosl2, Tgfbr3, Spint1, Dhrs3, Plec, Itga11, Tns3, Syt12, Dgkz, Unc13a, St3gal1, Nol4l, Fscn1, Cygb, Arhgef19, Ppl, Itgb5, Cobl, Col1a2, Osbp2, Ptpn14, Mbp, Nrp2, Cux1, Egflam, Atp2b4, Pitpnm3, Rap1gap, Bmper, Lama5, Camkk1, Plekhh3, Fkbp5, Itpr3, Abr, Slc27a1, Synj2, Wnt11, Cacna1a, Plxnd1, Fibcd1, Epb41, Prkcd, Myof, Olfm1, Epas1, Grm4, Aldh1a2, Gprc5c, Acot7, Nfic, Efna3, Nav1, Zbtb7c, Arhgef3, Susd1, Creb3l1, St14, Rapgef4, Hmcn2, Nacc2, Kcnk1, Cpne2, Efna1, Ccnd1, Gsn, Fgd5, Prkch, Ptprm, Pdgfb, Zfp385a, Ltbp3, 6430548M08Rik, Egln3, Galnt9, Adgre5, Tjp2, Hr, Epb41l1, Ndrg1, Garnl3, Ahnak, Tsc22d3, Unc5a, Cdh1, Wnt5a, Rasgrf1 E2f7 --> Vgll4, Ets1, Dst, Pdgfra, Zfp36l1 Foxm1 --> Foxf1, Ccnd2, Bmi1, Onecut1, Ccnd1 Pax6 --> Met, Ctnnd2 Srebf2 --> Acot7 Mta2 --> Epha2, Piezo1, Nol4l, Stat6 Hmga1 --> Cdh1, Twist1, Kl Sox11 --> Cdh11, Dst, Rapgef4, Ank2, Nedd4l Pbx2 --> Plcg2, Prkcb, Sox6, Meis1, 6430548M08Rik, Hoxb3, Nfasc, Tiam2, Garnl3 Arid1a --> Ptch1, Smarca2 Ctcf --> Etnk2, Celsr3, Ephb1, Rbms3, Igf2, Slc6a4, Col27a1, Aldh1a3, Wt1, Grin3a, B4galnt1, Abca2, Akt1, Ccnd1, App Gli2 --> Gli1, Foxe1, Foxa2, Wnt2b, Ptch1, Ccnd1 Gli1 --> Sfrp1, Mtss1, Pdgfra, Pde4b, Foxa2, Asic2, Kank1, Ccnd2, Wnt9a, Wnt2b, Ptch1, Bmp4, Npdc1, Wnt4, Pltp, Prkch, Wnt10b, Akt1, Foxa1, Ntrk2, Bhlhe41, Mtus1, Hhip, Wnt5a Tcf3 --> Piezo1 Twist1 --> Cdh1, Cldn7, Gli1 Cux1 --> Racgap1, Sptbn1, Srcin1, Macf1, Itgb5, Itgb4, Nfic Tcf7l2 --> Ccnd1, Sp5, Tcf7 Sox9 --> Nkx3-2, Comp, Cldn7, Hes1 Id3 --> Vgll4, Lingo1, Rreb1, Sh2b3, Alx4, Ptp4a2, Camk2b, Prkcb, Ahdc1, Atp2b2, Cpne5, Phldb1, Ank1, Gabbr2, Tgfbr3, Map3k5, Rtn4r, Tmem163, Slc12a7, Ltbp4, Plec, Astn2, Dgkz, Nol4l, Speg, Ppargc1b, Fscn1, Macf1, Nfix, Sox6, Mef2d, Itgb5, Kcp, Sdc3, Wt1, Atp2b4, Plagl1, Rnf220, Rap1gap, Lamc3, Dnajc6, Ror2, Synj2, Cacna1a, Mtss1, Ptpru, Fibcd1, Zc3hav1, Rab11fip1, Epas1, Adgrb2, Aldh1a2, Atp2a3, Cd34, Ntrk1, Acvr1b, Zfp36l1, Nav1, Osbpl6, Rassf5, Lrp2, Tbx1, Man1c1, Col15a1, Creb3l1, Dlgap4, Ass1, Hmcn2, Trank1, Kcnk1, Nfatc2, Asic4, Mical2, Gata2, Gfod1, Gsn, Pacsin1, Megf11, Gnaz, 6430548M08Rik, Egln3, Zfyve28, Phf19, Epb41l1, Ercc1, Piezo1, Tsc22d3, Fam89a, Ptk2b Tead2 --> Cybrd1, Speg, Ccnd2, Dlgap4, Fam89a, Rhbdf1 Dnmt1 --> Sfrp1, Gata4, Reln, Plekha6, Rgs10, Timp3, Cdkn2b, Epcam, Sfrp5, Cdh1 Trp53 --> Met, Scn3b, Cx3cl1, Gadd45g, Aldh1a3, Tgfb1, Ppm1j, Syk, Perp, Pmaip1, Epha2, Mapk8, Ccnd1, Egfr, Plagl1, Hic1, Flt1, Col18a1, Akt1, Igfbp3, Ndrg1, Nhlh2, Trp73, Sfn, Epcam, Tgfa, Tap1, Pax5 Maz --> Zbtb7a, Srcin1, Epb41l1, Cacna1a, Stat6, Ahdc1, Ctdsp1, Rnf220, Col1a1, Ppfia3, Dgkz Ybx1 --> Met, Egfr, Slc6a4, Cdh1, Erbb3 Usf1 --> Trim25, Hoxb7, Cebpa, Tgfb1, Hoxb4, Slc1a3 Smad3 --> Met, Ccnd2, Tnc, Tgfb1, Itga11, Mmp9, Col1a1, Pthlh Gli3 --> Shh, Mtss1, Wnt7b, Foxa2, Ptch1, Bmp4 Lhx2 --> Sox6, Ccnd1 Sox2 --> Eif4g3, Vgll4, Esrrg, Cacna2d3, Rbms3, Erbb4, Prune2, Pde4b, Sox6, Creb5, Ryr2, Adamtsl1, Prkg1, Ephb1, Sbf2, Skap2, Tenm3, Isl1, Csmd1, Elmo1, Mcc, Dab1, Sdk1, Ccnd1, Abcc4, Nrp2, Myo10, Ptprm, Csmd3, Alk, Ctnnd2, Piezo2, Cacna2d1, Nedd4l, Egln3, Cdh11, Col14a1, Ptprd, Col12a1, Opcml, Prr16, Cntn4, Nlgn1, Tns3, Spock1, Ebf1, App Nr2f2 --> Ppara, Penk, Acta1 --------------- Total for Development targeting Posterior: 424 ------------- Smarcc1 --> Cngb1, Adgrg3, Sh2d6, Rad51b, Tbxas1 Foxm1 --> Cdc25c, Cdc6 Ctcf --> C1ra, Lpin3, Mcm10 Gli1 --> Myo15 Tcf3 --> Pstpip1 Twist1 --> Timp1 Sox9 --> Col9a1 Id3 --> Prrx2, Nostrin, Cngb1, Tbxas1 Trp53 --> Chek2, Cdc25c, Chek1, Rad51b, Rad51, Tbxas1 Ybx1 --> Cks2, Cdkn3 Smad3 --> Timp1 Sox2 --> Rad51b, Diaph3 --------------- Total for Development targeting Immune response: 29 ------------- Smarcc1 --> Actb, Dock5, Plxnd1, Fscn1, Csnk1g2, Ptprf, Igf2bp3, Epb41, Nedd4, Ptp4a2, Tgfbi, Arid1a, Gatad2a, Ccnd1, Col1a2, Ptpn14, Cux1, Tjp2, Nav1, Col5a1, Ahnak, Plec, Smad3, Gli3, Ccnd3, Tns3, Sox2 E2f7 --> Hnrnpk, Rcc1, Lgals1, Arid1a, Uhrf1, Pdgfra, Hnrnpd, Dnmt1, E2f8, Zfp36l1 Foxm1 --> Aurkb, Plk1, Cenpf, Skp2, Ccnd2, Ccnb1, Cenpa, Cks1b, Birc5, Itgb1, Cdc6, Cdc25a, Ccnd1 Pax6 --> Itgb1, Itga5 Srebf2 --> Hmgcr, Acly Mta2 --> Hnrnpf, Ephb4, Tcf3, Hdgf Hmga1 --> Trp53, Twist1, Brca1, Cdc25a Sox11 --> Cdh11, Epha7 Pbx2 --> Ankrd11, Tle3, Dlg1 Arid1a --> Trip12, Ankrd11, Ptch1, Smad3 Ctcf --> Zfp462, Lrrc59, Igf2, Rad21, Rbm39, Nes, Mcm10, Ccnd1 Gli2 --> Gli1, Pcna, Ccnd1, Ptch1 Gli1 --> Sfrp1, Npdc1, Ccnd2, Sox9, Pdgfra, Gli2, Cdk2, Trp53, Ptch1 Tfdp1 --> Hnrnpul1, Dhfr, Cdk1, Tyms, Pcna, Ckap2l, Ptma, Mybl2, Rrm1 Tcf3 --> Anp32a Twist1 --> Gli1 Cux1 --> Racgap1, Eef2, Slc16a3, Ect2, Hspd1, Puf60 Tcf7l2 --> Gli2, Birc5, Vim, Ccnd1, Ctnnb1 Id3 --> Actb, Hnrnpl, Fscn1, Zc3h18, Ptprf, Ctnna1, Alx4, Ptp4a2, Arid1a, Pcbp1, Zfp36l2, Gatad2a, Tcf3, Nsd2, Ssbp3, Serpinh1, Plagl1, Rnf220, Zfp36l1, Hmgn2, Nav1, Slc12a7, Ephb4, Plec, Smad3, Ccnd3 Tead2 --> Snrpc, Ccnd2, Ajuba Dnmt1 --> Actb, Sfrp1, Hdac1, Rpl13, Birc5, Rpl10a, Trp53 Trp53 --> Ccna2, Hnrnpul1, Uhrf1, Vcan, Ccnb1, Rhoa, E2f7, Tyms, Birc5, Rrm1, Thbs1, Rad51, Ezh2, Ddb1, Cdk1, Brca1, Ccnd1, Hsp90ab1, Pold1, Lig1, Tcf7l2, Pcbp4, Pcna, Plagl1, Col18a1, Chek1, Plk1, Mki67, Mcm7, Dnmt1, Ctnnb1 Nono --> Polr2a, Hnrnpl, Sae1, Brd2, Prpf40a, Hnrnpd, Mcm7, Ankrd11 Maz --> Tacc3, Hnrnpf, Arid1a, Serpinh1, Rnf220, Col1a1, Tcf3 Ybx1 --> Top2a, Skp2, Kpna2, Cdc20, Ccnb1, Cks2, Tyms Ssrp1 --> Hnrnpa1, Rcc1, Rps15a, Eef2, Rps24, Ckap5, Lonp1, Rpl37a, Eno1, Hnrnpc, Hspd1 Usf1 --> Fasn, Nup188, Ccnb1, Cdk4, Tyms, Sf1, Serbp1 Smad3 --> Col3a1, Ccnd2, Col1a1, Gli2 Gli3 --> Smo, Ptch1, Gli2, Wnt7b Lhx2 --> Ccnd1 Sox2 --> Map1b, Cdh2, Cdh11, Sbf2, Trip12, Tenm3, Gli2, Myo10, Gli3, Jag1, Dlg1, Tns3, Ccnd1 --------------- Total for Development targeting Development: 243 ------------- Smarcc1 --> Gabre, Dennd3, Ahnak2, Phyhip, Cngb1, Rapgef4, Ttll6, Gdf15, Ldb3, Abcc3, Fam83f, Ccdc88b, Slc44a3, Hrh1, Osbp2, Ciita, Nlrc5, Trim47, Exoc3l4, Cdh23, Efna3, Fhad1, Kctd14, Sult2b1, Sh2d6, Rasgrf1, Tbxas1 E2f7 --> Fos Foxm1 --> Fos Pax6 --> Olfm3, Pcsk1 Hmga1 --> Kl, Cryab Sox11 --> Rapgef4, Rims1, Col28a1, Chodl, Atp10b Ctcf --> Etnk2, Prss35, Lpin3, Dmkn, Acvr1c, Zfp804b, Tnfrsf18, Cftr, Galnt6, C1ra, Ang, Wt1, Grin3a, Cd80, Bcl6, Chrna6 Gli2 --> Fas, Foxe1 Tgif2 --> Coq8a Gli1 --> Fas, Tnfrsf23, Itih3, Igfbp6, Myo15, Wnt10b, Bhlhe41, Spp1 Tcf3 --> Pstpip1, Ciita Twist1 --> Fos Cux1 --> Aloxe3 Tcf7l2 --> Fgf18 Sox9 --> Nkx3-2, Matn1, Mia, Psg16 Id3 --> Cngb1, Gdf15, Kcnn3, Abcc3, 5330417C22Rik, Pde4c, Ankrd55, Arhgap22, Ampd3, Ttc39a, Pacsin1, Zap70, Wt1, Myo3b, Nostrin, Grip2, Cdh23, Obscn, Fhad1, Zfyve28, Rassf5, Cmbl, Ubxn11, Sult2b1, Tbxas1 Tead2 --> Mpz Dnmt1 --> Slc7a11, Sfrp4, Gata4, Tnfrsf23, Runx3 Trp53 --> Fas, Tnfrsf23, Hgf, Fos, Pycard, Gdf15, Bcl6, S100b, Lif, Tbxas1 Maz --> Trim47 Ybx1 --> Fas, Mia Usf1 --> Ptgds, Slc22a2, Isg20, Ciita Smad3 --> Fstl3, Ahr, Mmp9 Mybl2 --> Psg16 Sox2 --> Kcnip4, Ryr2, Trpc4, Pacrg, Cpne4, Dlgap1, Ryr3, Ntm, Lrrc7, Anks1b, Csmd1, Slc44a3, Gria1, Clic5, Inpp4b, Lrp1b, Csmd3, Alk, Galntl6, Cdh18, Hivep3, Lsamp, Abi3bp, Ppargc1a, Dlg2, Opcml, Samd12, Dpp10, Cntn4, Nlgn1 Nr2f2 --> Apoa1, Myh6 --------------- Total for Development targeting Forebrain: 158 ------------- -------------------------------------------------------------------------------- Forebrain 18 Tfs: Hoxa11, Nr5a2, Gata4, Spdef, Hnf1b, Elf3, Batf, Fos, Wt1, Pgr, Rorc, Runx3, Runx2, Ahr, Bcl6, Msc, Rora, Hoxb13 -------------------------------------------------------------------------------- Spdef --> Sqstm1, Mmp9 Hnf1b --> Myo16, Nrxn1 Elf3 --> Atp2a2, Nedd4l, Rabgap1l, Nfia Batf --> Prkcb, Rabgap1l, Trim2, Kcnma1, Nrcam, Vopp1, Ncoa1 Fos --> Clu, Mmp9 Runx3 --> Fyn, Mmp9 Runx2 --> Plcb1, Psd3, Nedd4l, Tenm2, Tspan5, App Bcl6 --> Cbfa2t3 Rora --> Nr1d1 Hoxb13 --> Map1b, Thsd7a, Nfia, Dlg2, Psd3, Pde4d, Rit2, Sgip1, Nrxn1, Ryr2, Trio --------------- Total for Forebrain targeting Prolifferation: 38 ------------- Hoxa11 --> Hoxa10 Nr5a2 --> Tbc1d30, Fgd5, Kcnb1, Prkch, Myo10, Bmper, F11r Spdef --> Cdh1, Mmp9 Hnf1b --> Plekhg3, Mal2 Elf3 --> St14, Barx2, Mal2, Nedd4l, Meis1, Mcc, Plekhg3, Ptk2b Batf --> Prkch, Prkcb, Map3k5, St3gal1 Fos --> Ezr, Ercc1, Nefl, Bcl2l11, Acta1, Il4ra, Pgr, Pdgfb, Nfatc1, Maf, Gata3, Mmp9, Ntf3, Penk, Cdh1, Foxa1, Ccnd1 Wt1 --> Pdgfa, Ets1, Egfr, Igf2, Nr5a1, Cdh1, Pax2, Nphs1 Pgr --> Ccnd1 Runx3 --> Pax5, Tcf7, Mmp9 Runx2 --> Met, Cdh11, Col14a1, Rbms3, Tns1, Ets1, Ptprm, App, Mical2, Nedd4l, Rftn1, Tspan5, Myof, Tiam2 Ahr --> Vgll4, Cyp1b1, Erg Bcl6 --> Plcg2, St3gal1, Ccnd2, Ank3, Il4ra, Rnf213, Gfod1, Tns3 Hoxb13 --> Esrrg, Col14a1, Dock9, Dnah11, Ank3, Cntn4, Ryr2, Bnc2, Erg --------------- Total for Forebrain targeting Posterior: 87 ------------- Nr5a2 --> Fam160a1, Abcg3, Cyp11a1 Hnf1b --> Veph1 Batf --> Rhoh, Samsn1, Tbxas1 Fos --> Ccl2, Nat2 Runx2 --> Tbxas1 Ahr --> Ugt1a7c, Ugt1a6a Msc --> Cdc6 --------------- Total for Forebrain targeting Immune response: 13 ------------- Nr5a2 --> Txnrd1, Myo10 Elf3 --> Hk2 Fos --> Fasn, Ezr, Itga5, Cdk4, Trp53, Ccnd1 Wt1 --> Odc1, Igf2, Ube2i, Smad3, Thbs1 Pgr --> Ccnd1 Runx2 --> Cdh11, Gli3 Bcl6 --> Tle3, Ccnd2, Ank3, Ccnd3, Tns3, Tcf3 Msc --> Cdc25a, Cdc6, Mcm7 Hoxb13 --> Map1b, Ank3 --------------- Total for Forebrain targeting Development: 28 ------------- Nr5a2 --> Fam160a1, Abcg3, Cyp11a1 Gata4 --> Nppa Spdef --> Mmp9 Hnf1b --> Phex, Kcnip4, Mal2, Gpr160, B3galt5, Fxyd2 Elf3 --> Cftr, Ptpre, Barx2, Mal2, Slc44a3 Batf --> Gsap, Rhoh, Kcnma1, Tbxas1 Fos --> Fas, Nppa, Nos1, Ccl2, Pgr, Nts, Runx2, Mmp9 Runx3 --> Ciita, Mmp9 Runx2 --> Csgalnact1, Itgbl1, Ampd3, Lgals3, Tbxas1 Ahr --> Ugt1a7c, Fos, Ugt1a6a Bcl6 --> Dennd3 Msc --> Lta Hoxb13 --> Thrb, Dlg2, Rit2, Cntn4, Ryr2 --------------- Total for Forebrain targeting Forebrain: 45 ------------- E2f1 E2f4 Hif1a Myc forebrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF115VPJ_signal [nan] forebrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF592HST_signal [nan] forebrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF397HZX_signal [nan] forebrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF076ZNJ_signal [nan] forebrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF591TWD_signal [nan] forebrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF082ONF_signal [nan] forebrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF686XPK_signal [nan] hindbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF475JXJ_signal [nan] hindbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF809JLW_signal [nan] hindbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF435HPM_signal [nan] hindbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF324KBG_signal [nan] hindbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF658ZBC_signal [nan] hindbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF086VKO_signal [nan] hindbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF787IGD_signal [nan] midbrain_10.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF942MEP_signal [2.27189] midbrain_11.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF041KRQ_signal [nan] midbrain_12.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF635JKX_signal [nan] midbrain_13.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF276UEL_signal [nan] midbrain_14.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF402CBS_signal [nan] midbrain_15.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF962ZLC_signal [nan] midbrain_16.5-days_embryonic_H3K27me3_ChIP-seq_ENCFF887CCI_signal [nan]
Here we test other summary methods for gene separability.
from scipy import stats
import random
def get_shallow_vae(values, num_nodes):
config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 1.0},
'encoding': {'layers': []},
'decoding': {'layers': []},
'latent': {'num_nodes': num_nodes}, 'optimiser': {'params': {}, 'name': 'adam'},
'seed': random.randint(0,100)} # ensure there is a random seed just like in UMAP and TSNE
vae_shallow = VAE(values, values, labels, config, f'vae_{num_nodes}')
vae_shallow.encode('default', epochs=100, batch_size=50)
# Encode the same using the linear vae
vae_data_shallow = vae_shallow.encode_new_data(values)
return vae_data_shallow
def get_deep_vae(values, num_nodes):
config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 1.0,
'mmcd_method': 'k'},
'encoding': {'layers': [{'num_nodes': 64, 'activation_fn': 'selu'},
{'num_nodes': 32, 'activation_fn': 'relu'}
]},
'decoding': {'layers': [
{'num_nodes': 32, 'activation_fn': 'relu'},
{'num_nodes': 64, 'activation_fn': 'selu'}]},
'latent': {'num_nodes': num_nodes}, 'optimiser': {'params': {}, 'name': 'adam'},
'seed': random.randint(0,100)}
vae_mse = VAE(values, values, labels, config, f'runs')
vae_mse.encode('default', epochs=100, batch_size=50)
vae_data = vae_mse.encode_new_data(values)
return vae_data
def compute_dists(grp1, grp2, data):
grp1_idxs = []
grp2_idxs = []
i = 0
for g in df[gene_name].values:
if g in grp1:
grp1_idxs.append(i)
if g in grp2:
grp2_idxs.append(i)
i += 1
centroid_grp1 = np.mean(data[grp1_idxs, :], axis=0)
grp_1_within_dist = np.sqrt(np.sum((data[grp1_idxs, :] - centroid_grp1) **2, axis=1))
centroid_grp2 = np.mean(data[grp2_idxs, :], axis=0)
grp_2_within_dist = np.sqrt(np.sum((data[grp2_idxs, :] - centroid_grp2) **2, axis=1))
all_idxs = grp1_idxs + grp2_idxs
centroid = np.mean(data[all_idxs, :], axis=0)
between_dist = np.sqrt(np.sum((data[all_idxs, :] - centroid) **2, axis=1))
t1_stat, t1_pval = stats.ttest_ind(grp_1_within_dist, between_dist)
t2_stat, t2_pval = stats.ttest_ind(grp_2_within_dist, between_dist)
# If both t1 and t2 are sig return that it was a success
if t1_pval < 0.05 and t2_pval < 0.05:
return 1
else:
return 0
import random
def get_shallow_vae(values, num_nodes):
config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 1.0},
'encoding': {'layers': []},
'decoding': {'layers': []},
'latent': {'num_nodes': num_nodes}, 'optimiser': {'params': {}, 'name': 'adam'},
'seed': random.randint(0,100)} # ensure there is a random seed just like in UMAP and TSNE
vae_shallow = VAE(values, values, labels, config, f'vae_{num_nodes}')
vae_shallow.encode('default', epochs=100, batch_size=50)
# Encode the same using the linear vae
vae_data_shallow = vae_shallow.encode_new_data(values)
return vae_data_shallow, vae_shallow
def get_deep_vae(values, num_nodes):
config = {'loss': {'loss_type': 'mse', 'distance_metric': 'mmd', 'mmd_weight': 1.0,
'mmcd_method': 'k'},
'encoding': {'layers': [{'num_nodes': 64, 'activation_fn': 'selu'},
{'num_nodes': 32, 'activation_fn': 'relu'}
]},
'decoding': {'layers': [
{'num_nodes': 32, 'activation_fn': 'relu'},
{'num_nodes': 64, 'activation_fn': 'selu'}]},
'latent': {'num_nodes': num_nodes}, 'optimiser': {'params': {}, 'name': 'adam'},
'seed': random.randint(0,100)}
vae_mse = VAE(values, values, labels, config, f'runs')
vae_mse.encode('default', epochs=100, batch_size=50)
vae_data = vae_mse.encode_new_data(values)
return vae_data, vae_mse
results_indep = {}
results_tog = {}
results_t = {}
fb_genes = ['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6', 'Arx', 'Dlx1', 'Dlx2', 'Dlx5', 'Nr2e2', 'Otx2']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b']
sc_genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 'Hoxa13',
'Hoxb9', 'Hoxb13', 'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']
ns_glutamatergic = ['Slc17a6', 'VGLUT2', 'Slc17a7', 'VGLUT1', 'Slc17a8', 'VGLUT3', 'Slc1a1', 'Slc1a2', 'Slc1a6']
ns_progenitors = ['Sox2', 'Hes1', 'Hes5', 'Vim']
prolif = ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a',
'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a']
comp = ['SC and FB',
'SC and MB',
'SC and HB',
'SC and Pro.',
'MB and FB',
'MB and HB',
'MB and Pro.',
'HB and FB',
'HB and Pro.',
'FB and Pro.']
for c in comp:
results_indep[c] = {}
results_tog[c] = {}
results_t[c] = {}
for label, data in data_m.items():
results_indep[c][label] = 0
results_tog[c][label] = 0
results_t[c][label] = 0
t_tsts = 0
i_tsts = 0
# Keep track of the generated results
data_ms = []
data_transforms = []
for tn in range(0, 30):
## Do a number of tests to compare how we'll each method is able to
## reproduce the
phate_op = phate.PHATE(n_components=3, random_state=random.randint(0,100))
phate_data = phate_op.fit_transform(vae_input_values)
umap_op = umap.UMAP(n_components=3,
random_state=random.randint(0,100),
transform_seed=random.randint(0,100))
umap_data = umap_op.fit_transform(vae_input_values)
pca_op = PCA(n_components=3)
pca_data = pca_op.fit_transform(vae_input_values)
deep_vae_data, d_vae = get_deep_vae(vae_input_values, 3)
shallow_vae_data, s_vae = get_shallow_vae(vae_input_values, 3)
tsne_op = TSNE(n_components=3, random_state=random.randint(0,100))
tsne_data = tsne_op.fit_transform(vae_input_values)
data_m = {
'VAE-deep': deep_vae_data,
'VAE-lin': shallow_vae_data,
'PCA': pca_data,
'UMAP': umap_data,
'PHATE': phate_data,
'TSNE': tsne_data
}
data_trans = {
'VAE-deep': d_vae,
'VAE-lin': s_vae,
'PCA': pca_op,
'UMAP': umap_op,
'PHATE': phate_op,
'TSNE': tsne_op
}
# Add the data to our list
data_ms.append(data_m)
data_transforms.append(data_trans)
t_tsts += 1
i_tsts += 3
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 11.51 seconds.
Calculating affinities...
Calculated affinities in 0.21 seconds.
Calculated graph and diffusion operator in 11.74 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.76 seconds.
Calculating KMeans...
Calculated KMeans in 13.62 seconds.
Calculated landmark operator in 15.57 seconds.
Calculating optimal t...
Automatically selected t = 35
Calculated optimal t in 2.52 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.54 seconds.
Calculating metric MDS...
Calculated metric MDS in 15.63 seconds.
Calculated PHATE in 46.01 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_263 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_264 (Dense) (None, 32) 2080 dense_263[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_264[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_264[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_265 (Dense) (None, 32) 128
_________________________________________________________________
dense_266 (Dense) (None, 64) 2112
_________________________________________________________________
dense_267 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_263 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_264 (Dense) (None, 32) 2080 dense_263[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_264[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_264[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_870 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_870 ( [()] 0 tf_op_layer_Shape_870[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_87 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_870[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_87[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_261 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_87 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_261[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_871 (TensorFl [(2,)] 0 tf_op_layer_Add_87[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_873 (TensorFl [(2,)] 0 tf_op_layer_Add_87[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_872 (TensorFl [(2,)] 0 tf_op_layer_Add_87[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_874 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_876 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_875 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_877 (TensorFl [(2,)] 0 tf_op_layer_Add_87[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_879 (TensorFl [(2,)] 0 tf_op_layer_Add_87[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_878 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_871 ( [()] 0 tf_op_layer_Shape_871[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_873 ( [()] 0 tf_op_layer_Shape_873[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_872 ( [()] 0 tf_op_layer_Shape_872[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_874 ( [()] 0 tf_op_layer_Shape_874[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_876 ( [()] 0 tf_op_layer_Shape_876[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_875 ( [()] 0 tf_op_layer_Shape_875[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_877 ( [()] 0 tf_op_layer_Shape_877[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_879 ( [()] 0 tf_op_layer_Shape_879[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_878 ( [()] 0 tf_op_layer_Shape_878[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_522/shape ( [(3,)] 0 tf_op_layer_strided_slice_871[0][
tf_op_layer_strided_slice_873[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_523/shape ( [(3,)] 0 tf_op_layer_strided_slice_872[0][
tf_op_layer_strided_slice_873[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_524/shape ( [(3,)] 0 tf_op_layer_strided_slice_874[0][
tf_op_layer_strided_slice_876[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_525/shape ( [(3,)] 0 tf_op_layer_strided_slice_875[0][
tf_op_layer_strided_slice_876[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_526/shape ( [(3,)] 0 tf_op_layer_strided_slice_877[0][
tf_op_layer_strided_slice_879[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_527/shape ( [(3,)] 0 tf_op_layer_strided_slice_878[0][
tf_op_layer_strided_slice_879[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_522 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_87[0][0]
tf_op_layer_Reshape_522/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_522/multiples [(3,)] 0 tf_op_layer_strided_slice_872[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_523 (Tensor [(1, None, None)] 0 tf_op_layer_Add_87[0][0]
tf_op_layer_Reshape_523/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_523/multiples [(3,)] 0 tf_op_layer_strided_slice_871[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_524 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_524/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_524/multiples [(3,)] 0 tf_op_layer_strided_slice_875[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_525 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_525/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_525/multiples [(3,)] 0 tf_op_layer_strided_slice_874[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_526 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_87[0][0]
tf_op_layer_Reshape_526/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_526/multiples [(3,)] 0 tf_op_layer_strided_slice_878[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_527 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_527/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_527/multiples [(3,)] 0 tf_op_layer_strided_slice_877[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_522 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_522[0][0]
tf_op_layer_Tile_522/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_523 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_523[0][0]
tf_op_layer_Tile_523/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_524 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_524[0][0]
tf_op_layer_Tile_524/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_525 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_525[0][0]
tf_op_layer_Tile_525/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_526 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_526[0][0]
tf_op_layer_Tile_526/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_527 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_527[0][0]
tf_op_layer_Tile_527/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_435 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_522[0][0]
tf_op_layer_Tile_523[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_436 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_524[0][0]
tf_op_layer_Tile_525[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_437 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_526[0][0]
tf_op_layer_Tile_527[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_348 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_435[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_349 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_436[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_350 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_437[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_609 (TensorFlo [(None, None)] 0 tf_op_layer_Square_348[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_610 (TensorFlo [(None, None)] 0 tf_op_layer_Square_349[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_611 (TensorFlo [(None, None)] 0 tf_op_layer_Square_350[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_261 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_609[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_261 (TensorFlo [()] 0 tf_op_layer_strided_slice_873[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_262 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_610[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_262 (TensorFlo [()] 0 tf_op_layer_strided_slice_876[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_263 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_611[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_263 (TensorFlo [()] 0 tf_op_layer_strided_slice_879[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_261 (Tensor [(None, None)] 0 tf_op_layer_Neg_261[0][0]
tf_op_layer_Cast_261[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_262 (Tensor [(None, None)] 0 tf_op_layer_Neg_262[0][0]
tf_op_layer_Cast_262[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_263 (Tensor [(None, None)] 0 tf_op_layer_Neg_263[0][0]
tf_op_layer_Cast_263[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_261 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_261[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_262 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_262[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_263 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_263[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_612 (TensorFlo [()] 0 tf_op_layer_Exp_261[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_613 (TensorFlo [()] 0 tf_op_layer_Exp_262[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_614 (TensorFlo [()] 0 tf_op_layer_Exp_263[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_439 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_174 (TensorFl [()] 0 tf_op_layer_Mean_612[0][0]
tf_op_layer_Mean_613[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_262 (TensorFlow [()] 0 tf_op_layer_Mean_614[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_351 (TensorF [(None, 97)] 0 tf_op_layer_Sub_439[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_438 (TensorFlow [()] 0 tf_op_layer_AddV2_174[0][0]
tf_op_layer_Mul_262[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_87 (TensorFlowO [(None,)] 0 tf_op_layer_Square_351[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_263 (TensorFlow [()] 0 tf_op_layer_Sub_438[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_175 (TensorFl [(None,)] 0 tf_op_layer_Sum_87[0][0]
tf_op_layer_Mul_263[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_615 (TensorFlo [()] 0 tf_op_layer_AddV2_175[0][0]
__________________________________________________________________________________________________
add_loss_87 (AddLoss) () 0 tf_op_layer_Mean_615[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 8.3181WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1010s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 0.9437 - val_loss: 0.5115
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4913 - val_loss: 0.4400
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4080 - val_loss: 0.3860
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3853 - val_loss: 0.3770
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3790 - val_loss: 0.3725
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3745 - val_loss: 0.3664
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3705 - val_loss: 0.3641
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3667 - val_loss: 0.3629
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3644 - val_loss: 0.3621
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3618 - val_loss: 0.3577
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3604 - val_loss: 0.3561
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3585 - val_loss: 0.3559
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3576 - val_loss: 0.3513
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3555 - val_loss: 0.3568
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3550 - val_loss: 0.3517
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3499
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3540 - val_loss: 0.3549
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3518 - val_loss: 0.3483
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3479
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3516 - val_loss: 0.3475
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3508 - val_loss: 0.3471
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3506 - val_loss: 0.3479
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3504
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3493
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3494
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3439
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3455
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3485
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3484 - val_loss: 0.3477
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3458
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3513
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3446
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3453
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3448
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3464
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3450
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3459
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3479
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3475
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3426
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3438
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3440
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3431
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3422
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3461
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3458
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3408
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3417
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3431
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3414
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3406
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3429
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3394
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3425
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3407
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3398
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3424
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3462
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3441
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3434
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3419
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3401
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3423
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3404
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3409
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3411
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3431
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3421
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3402
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3430
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3436
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3410
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3396
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3435
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3400 - val_loss: 0.3417
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3401
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3411
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3389 - val_loss: 0.3442
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3420
Epoch 80/100
218/218 [==============================] - 0s 992us/step - loss: 0.3406 - val_loss: 0.3409
Epoch 81/100
218/218 [==============================] - 0s 982us/step - loss: 0.3399 - val_loss: 0.3400
Epoch 82/100
218/218 [==============================] - 0s 988us/step - loss: 0.3403 - val_loss: 0.3393
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3410
Epoch 84/100
218/218 [==============================] - 0s 987us/step - loss: 0.3399 - val_loss: 0.3402
Epoch 85/100
218/218 [==============================] - 0s 989us/step - loss: 0.3403 - val_loss: 0.3409
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3413
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3392
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3392 - val_loss: 0.3411
Epoch 89/100
218/218 [==============================] - 0s 978us/step - loss: 0.3393 - val_loss: 0.3384
Epoch 90/100
218/218 [==============================] - 0s 963us/step - loss: 0.3387 - val_loss: 0.3381
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3414
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3402
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3392 - val_loss: 0.3402
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3386 - val_loss: 0.3400
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3379 - val_loss: 0.3395
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3398
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3379
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3392 - val_loss: 0.3391
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3377
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3382 - val_loss: 0.3377
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_268 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_880 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_880 ( [()] 0 tf_op_layer_Shape_880[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_88 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_880[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_88[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_264 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_88 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_264[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_881 (TensorFl [(2,)] 0 tf_op_layer_Add_88[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_883 (TensorFl [(2,)] 0 tf_op_layer_Add_88[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_882 (TensorFl [(2,)] 0 tf_op_layer_Add_88[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_884 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_886 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_885 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_887 (TensorFl [(2,)] 0 tf_op_layer_Add_88[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_889 (TensorFl [(2,)] 0 tf_op_layer_Add_88[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_888 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_881 ( [()] 0 tf_op_layer_Shape_881[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_883 ( [()] 0 tf_op_layer_Shape_883[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_882 ( [()] 0 tf_op_layer_Shape_882[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_884 ( [()] 0 tf_op_layer_Shape_884[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_886 ( [()] 0 tf_op_layer_Shape_886[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_885 ( [()] 0 tf_op_layer_Shape_885[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_887 ( [()] 0 tf_op_layer_Shape_887[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_889 ( [()] 0 tf_op_layer_Shape_889[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_888 ( [()] 0 tf_op_layer_Shape_888[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_528/shape ( [(3,)] 0 tf_op_layer_strided_slice_881[0][
tf_op_layer_strided_slice_883[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_529/shape ( [(3,)] 0 tf_op_layer_strided_slice_882[0][
tf_op_layer_strided_slice_883[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_530/shape ( [(3,)] 0 tf_op_layer_strided_slice_884[0][
tf_op_layer_strided_slice_886[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_531/shape ( [(3,)] 0 tf_op_layer_strided_slice_885[0][
tf_op_layer_strided_slice_886[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_532/shape ( [(3,)] 0 tf_op_layer_strided_slice_887[0][
tf_op_layer_strided_slice_889[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_533/shape ( [(3,)] 0 tf_op_layer_strided_slice_888[0][
tf_op_layer_strided_slice_889[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_528 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_88[0][0]
tf_op_layer_Reshape_528/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_528/multiples [(3,)] 0 tf_op_layer_strided_slice_882[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_529 (Tensor [(1, None, None)] 0 tf_op_layer_Add_88[0][0]
tf_op_layer_Reshape_529/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_529/multiples [(3,)] 0 tf_op_layer_strided_slice_881[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_530 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_530/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_530/multiples [(3,)] 0 tf_op_layer_strided_slice_885[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_531 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_531/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_531/multiples [(3,)] 0 tf_op_layer_strided_slice_884[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_532 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_88[0][0]
tf_op_layer_Reshape_532/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_532/multiples [(3,)] 0 tf_op_layer_strided_slice_888[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_533 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_533/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_533/multiples [(3,)] 0 tf_op_layer_strided_slice_887[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_528 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_528[0][0]
tf_op_layer_Tile_528/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_529 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_529[0][0]
tf_op_layer_Tile_529/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_530 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_530[0][0]
tf_op_layer_Tile_530/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_531 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_531[0][0]
tf_op_layer_Tile_531/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_532 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_532[0][0]
tf_op_layer_Tile_532/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_533 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_533[0][0]
tf_op_layer_Tile_533/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_440 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_528[0][0]
tf_op_layer_Tile_529[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_441 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_530[0][0]
tf_op_layer_Tile_531[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_442 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_532[0][0]
tf_op_layer_Tile_533[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_352 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_440[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_353 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_441[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_354 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_442[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_616 (TensorFlo [(None, None)] 0 tf_op_layer_Square_352[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_617 (TensorFlo [(None, None)] 0 tf_op_layer_Square_353[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_618 (TensorFlo [(None, None)] 0 tf_op_layer_Square_354[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_264 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_616[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_264 (TensorFlo [()] 0 tf_op_layer_strided_slice_883[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_265 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_617[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_265 (TensorFlo [()] 0 tf_op_layer_strided_slice_886[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_266 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_618[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_266 (TensorFlo [()] 0 tf_op_layer_strided_slice_889[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_264 (Tensor [(None, None)] 0 tf_op_layer_Neg_264[0][0]
tf_op_layer_Cast_264[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_265 (Tensor [(None, None)] 0 tf_op_layer_Neg_265[0][0]
tf_op_layer_Cast_265[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_266 (Tensor [(None, None)] 0 tf_op_layer_Neg_266[0][0]
tf_op_layer_Cast_266[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_264 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_264[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_265 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_265[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_266 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_266[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_619 (TensorFlo [()] 0 tf_op_layer_Exp_264[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_620 (TensorFlo [()] 0 tf_op_layer_Exp_265[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_621 (TensorFlo [()] 0 tf_op_layer_Exp_266[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_444 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_176 (TensorFl [()] 0 tf_op_layer_Mean_619[0][0]
tf_op_layer_Mean_620[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_265 (TensorFlow [()] 0 tf_op_layer_Mean_621[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_355 (TensorF [(None, 97)] 0 tf_op_layer_Sub_444[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_443 (TensorFlow [()] 0 tf_op_layer_AddV2_176[0][0]
tf_op_layer_Mul_265[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_88 (TensorFlowO [(None,)] 0 tf_op_layer_Square_355[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_266 (TensorFlow [()] 0 tf_op_layer_Sub_443[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_177 (TensorFl [(None,)] 0 tf_op_layer_Sum_88[0][0]
tf_op_layer_Mul_266[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_622 (TensorFlo [()] 0 tf_op_layer_AddV2_177[0][0]
__________________________________________________________________________________________________
add_loss_88 (AddLoss) () 0 tf_op_layer_Mean_622[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 8.2908WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.1183s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9477 - val_loss: 0.9770
Epoch 2/100
218/218 [==============================] - 0s 903us/step - loss: 0.8234 - val_loss: 0.7236
Epoch 3/100
218/218 [==============================] - 0s 903us/step - loss: 0.6868 - val_loss: 0.6490
Epoch 4/100
218/218 [==============================] - 0s 901us/step - loss: 0.6308 - val_loss: 0.6087
Epoch 5/100
218/218 [==============================] - 0s 897us/step - loss: 0.6014 - val_loss: 0.5826
Epoch 6/100
218/218 [==============================] - 0s 904us/step - loss: 0.5835 - val_loss: 0.5687
Epoch 7/100
218/218 [==============================] - 0s 904us/step - loss: 0.5658 - val_loss: 0.5530
Epoch 8/100
218/218 [==============================] - 0s 909us/step - loss: 0.5555 - val_loss: 0.5528
Epoch 9/100
218/218 [==============================] - 0s 905us/step - loss: 0.5478 - val_loss: 0.5414
Epoch 10/100
218/218 [==============================] - 0s 910us/step - loss: 0.5397 - val_loss: 0.5327
Epoch 11/100
218/218 [==============================] - 0s 906us/step - loss: 0.5326 - val_loss: 0.5193
Epoch 12/100
218/218 [==============================] - 0s 905us/step - loss: 0.5272 - val_loss: 0.5162
Epoch 13/100
218/218 [==============================] - 0s 922us/step - loss: 0.5226 - val_loss: 0.5112
Epoch 14/100
218/218 [==============================] - 0s 908us/step - loss: 0.5207 - val_loss: 0.5141
Epoch 15/100
218/218 [==============================] - 0s 914us/step - loss: 0.5195 - val_loss: 0.5046
Epoch 16/100
218/218 [==============================] - 0s 881us/step - loss: 0.5142 - val_loss: 0.5085
Epoch 17/100
218/218 [==============================] - 0s 919us/step - loss: 0.5125 - val_loss: 0.5034
Epoch 18/100
218/218 [==============================] - 0s 913us/step - loss: 0.5061 - val_loss: 0.5008
Epoch 19/100
218/218 [==============================] - 0s 917us/step - loss: 0.5064 - val_loss: 0.4964
Epoch 20/100
218/218 [==============================] - 0s 919us/step - loss: 0.5054 - val_loss: 0.4935
Epoch 21/100
218/218 [==============================] - 0s 910us/step - loss: 0.4988 - val_loss: 0.4914
Epoch 22/100
218/218 [==============================] - 0s 907us/step - loss: 0.4981 - val_loss: 0.4903
Epoch 23/100
218/218 [==============================] - 0s 912us/step - loss: 0.4977 - val_loss: 0.4885
Epoch 24/100
218/218 [==============================] - 0s 909us/step - loss: 0.4966 - val_loss: 0.4849
Epoch 25/100
218/218 [==============================] - 0s 903us/step - loss: 0.4944 - val_loss: 0.4843
Epoch 26/100
218/218 [==============================] - 0s 907us/step - loss: 0.4941 - val_loss: 0.4823
Epoch 27/100
218/218 [==============================] - 0s 925us/step - loss: 0.4914 - val_loss: 0.4860
Epoch 28/100
218/218 [==============================] - 0s 920us/step - loss: 0.4899 - val_loss: 0.4803
Epoch 29/100
218/218 [==============================] - 0s 913us/step - loss: 0.4875 - val_loss: 0.4796
Epoch 30/100
218/218 [==============================] - 0s 912us/step - loss: 0.4864 - val_loss: 0.4788
Epoch 31/100
218/218 [==============================] - 0s 907us/step - loss: 0.4890 - val_loss: 0.4731
Epoch 32/100
218/218 [==============================] - 0s 910us/step - loss: 0.4846 - val_loss: 0.4767
Epoch 33/100
218/218 [==============================] - 0s 916us/step - loss: 0.4873 - val_loss: 0.4765
Epoch 34/100
218/218 [==============================] - 0s 906us/step - loss: 0.4850 - val_loss: 0.4754
Epoch 35/100
218/218 [==============================] - 0s 905us/step - loss: 0.4843 - val_loss: 0.4732
Epoch 36/100
218/218 [==============================] - 0s 906us/step - loss: 0.4813 - val_loss: 0.4691
Epoch 37/100
218/218 [==============================] - 0s 911us/step - loss: 0.4803 - val_loss: 0.4705
Epoch 38/100
218/218 [==============================] - 0s 911us/step - loss: 0.4805 - val_loss: 0.4719
Epoch 39/100
218/218 [==============================] - 0s 907us/step - loss: 0.4789 - val_loss: 0.4741
Epoch 40/100
218/218 [==============================] - 0s 912us/step - loss: 0.4782 - val_loss: 0.4677
Epoch 41/100
218/218 [==============================] - 0s 908us/step - loss: 0.4813 - val_loss: 0.4719
Epoch 42/100
218/218 [==============================] - 0s 909us/step - loss: 0.4779 - val_loss: 0.4680
Epoch 43/100
218/218 [==============================] - 0s 913us/step - loss: 0.4792 - val_loss: 0.4710
Epoch 44/100
218/218 [==============================] - 0s 905us/step - loss: 0.4776 - val_loss: 0.4660
Epoch 45/100
218/218 [==============================] - 0s 911us/step - loss: 0.4757 - val_loss: 0.4664
Epoch 46/100
218/218 [==============================] - 0s 910us/step - loss: 0.4767 - val_loss: 0.4693
Epoch 47/100
218/218 [==============================] - 0s 911us/step - loss: 0.4775 - val_loss: 0.4663
Epoch 48/100
218/218 [==============================] - 0s 921us/step - loss: 0.4753 - val_loss: 0.4667
Epoch 49/100
218/218 [==============================] - 0s 911us/step - loss: 0.4740 - val_loss: 0.4695
Epoch 50/100
218/218 [==============================] - 0s 938us/step - loss: 0.4734 - val_loss: 0.4658
Epoch 51/100
218/218 [==============================] - 0s 940us/step - loss: 0.4744 - val_loss: 0.4662
Epoch 52/100
218/218 [==============================] - 0s 936us/step - loss: 0.4733 - val_loss: 0.4666
Epoch 53/100
218/218 [==============================] - 0s 941us/step - loss: 0.4727 - val_loss: 0.4654
Epoch 54/100
218/218 [==============================] - 0s 952us/step - loss: 0.4725 - val_loss: 0.4673
Epoch 55/100
218/218 [==============================] - 0s 903us/step - loss: 0.4707 - val_loss: 0.4679
Epoch 56/100
218/218 [==============================] - 0s 953us/step - loss: 0.4731 - val_loss: 0.4619
Epoch 57/100
218/218 [==============================] - 0s 936us/step - loss: 0.4712 - val_loss: 0.4688
Epoch 58/100
218/218 [==============================] - 0s 945us/step - loss: 0.4710 - val_loss: 0.4654
Epoch 59/100
218/218 [==============================] - 0s 932us/step - loss: 0.4707 - val_loss: 0.4654
Epoch 60/100
218/218 [==============================] - 0s 935us/step - loss: 0.4704 - val_loss: 0.4661
Epoch 61/100
218/218 [==============================] - 0s 932us/step - loss: 0.4695 - val_loss: 0.4649
Epoch 62/100
218/218 [==============================] - 0s 939us/step - loss: 0.4704 - val_loss: 0.4618
Epoch 63/100
218/218 [==============================] - 0s 946us/step - loss: 0.4703 - val_loss: 0.4580
Epoch 64/100
218/218 [==============================] - 0s 942us/step - loss: 0.4708 - val_loss: 0.4602
Epoch 65/100
218/218 [==============================] - 0s 938us/step - loss: 0.4723 - val_loss: 0.4618
Epoch 66/100
218/218 [==============================] - 0s 924us/step - loss: 0.4696 - val_loss: 0.4595
Epoch 67/100
218/218 [==============================] - 0s 955us/step - loss: 0.4687 - val_loss: 0.4658
Epoch 68/100
218/218 [==============================] - 0s 936us/step - loss: 0.4672 - val_loss: 0.4625
Epoch 69/100
218/218 [==============================] - 0s 929us/step - loss: 0.4695 - val_loss: 0.4625
Epoch 70/100
218/218 [==============================] - 0s 938us/step - loss: 0.4678 - val_loss: 0.4600
Epoch 71/100
218/218 [==============================] - 0s 949us/step - loss: 0.4680 - val_loss: 0.4579
Epoch 72/100
218/218 [==============================] - 0s 917us/step - loss: 0.4674 - val_loss: 0.4605
Epoch 73/100
218/218 [==============================] - 0s 955us/step - loss: 0.4669 - val_loss: 0.4590
Epoch 74/100
218/218 [==============================] - 0s 956us/step - loss: 0.4655 - val_loss: 0.4662
Epoch 75/100
218/218 [==============================] - 0s 934us/step - loss: 0.4675 - val_loss: 0.4584
Epoch 76/100
218/218 [==============================] - 0s 933us/step - loss: 0.4646 - val_loss: 0.4582
Epoch 77/100
218/218 [==============================] - 0s 965us/step - loss: 0.4647 - val_loss: 0.4597
Epoch 78/100
218/218 [==============================] - 0s 974us/step - loss: 0.4681 - val_loss: 0.4624
Epoch 79/100
218/218 [==============================] - 0s 964us/step - loss: 0.4674 - val_loss: 0.4577
Epoch 80/100
218/218 [==============================] - 0s 952us/step - loss: 0.4664 - val_loss: 0.4588
Epoch 81/100
218/218 [==============================] - 0s 942us/step - loss: 0.4654 - val_loss: 0.4548
Epoch 82/100
218/218 [==============================] - 0s 982us/step - loss: 0.4651 - val_loss: 0.4615
Epoch 83/100
218/218 [==============================] - 0s 972us/step - loss: 0.4675 - val_loss: 0.4560
Epoch 84/100
218/218 [==============================] - 0s 963us/step - loss: 0.4656 - val_loss: 0.4586
Epoch 85/100
218/218 [==============================] - 0s 965us/step - loss: 0.4649 - val_loss: 0.4600
Epoch 86/100
218/218 [==============================] - 0s 964us/step - loss: 0.4639 - val_loss: 0.4527
Epoch 87/100
218/218 [==============================] - 0s 962us/step - loss: 0.4656 - val_loss: 0.4560
Epoch 88/100
218/218 [==============================] - 0s 969us/step - loss: 0.4665 - val_loss: 0.4532
Epoch 89/100
218/218 [==============================] - 0s 965us/step - loss: 0.4643 - val_loss: 0.4547
Epoch 90/100
218/218 [==============================] - 0s 975us/step - loss: 0.4617 - val_loss: 0.4522
Epoch 91/100
218/218 [==============================] - 0s 995us/step - loss: 0.4651 - val_loss: 0.4571
Epoch 92/100
218/218 [==============================] - 0s 975us/step - loss: 0.4652 - val_loss: 0.4579
Epoch 93/100
218/218 [==============================] - 0s 961us/step - loss: 0.4638 - val_loss: 0.4562
Epoch 94/100
218/218 [==============================] - 0s 969us/step - loss: 0.4651 - val_loss: 0.4540
Epoch 95/100
218/218 [==============================] - 0s 964us/step - loss: 0.4633 - val_loss: 0.4591
Epoch 96/100
218/218 [==============================] - 0s 972us/step - loss: 0.4638 - val_loss: 0.4584
Epoch 97/100
218/218 [==============================] - 0s 962us/step - loss: 0.4625 - val_loss: 0.4545
Epoch 98/100
218/218 [==============================] - 0s 965us/step - loss: 0.4618 - val_loss: 0.4569
Epoch 99/100
218/218 [==============================] - 0s 969us/step - loss: 0.4647 - val_loss: 0.4584
Epoch 100/100
218/218 [==============================] - 0s 993us/step - loss: 0.4612 - val_loss: 0.4536
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 12.97 seconds.
Calculating affinities...
Calculated affinities in 0.31 seconds.
Calculated graph and diffusion operator in 13.29 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.86 seconds.
Calculating KMeans...
Calculated KMeans in 15.46 seconds.
Calculated landmark operator in 17.73 seconds.
Calculating optimal t...
Automatically selected t = 32
Calculated optimal t in 2.80 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.44 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.66 seconds.
Calculated PHATE in 50.94 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_269 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_270 (Dense) (None, 32) 2080 dense_269[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_270[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_270[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_271 (Dense) (None, 32) 128
_________________________________________________________________
dense_272 (Dense) (None, 64) 2112
_________________________________________________________________
dense_273 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_269 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_270 (Dense) (None, 32) 2080 dense_269[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_270[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_270[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_890 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_890 ( [()] 0 tf_op_layer_Shape_890[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_89 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_890[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_89[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_267 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_89 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_267[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_891 (TensorFl [(2,)] 0 tf_op_layer_Add_89[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_893 (TensorFl [(2,)] 0 tf_op_layer_Add_89[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_892 (TensorFl [(2,)] 0 tf_op_layer_Add_89[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_894 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_896 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_895 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_897 (TensorFl [(2,)] 0 tf_op_layer_Add_89[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_899 (TensorFl [(2,)] 0 tf_op_layer_Add_89[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_898 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_891 ( [()] 0 tf_op_layer_Shape_891[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_893 ( [()] 0 tf_op_layer_Shape_893[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_892 ( [()] 0 tf_op_layer_Shape_892[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_894 ( [()] 0 tf_op_layer_Shape_894[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_896 ( [()] 0 tf_op_layer_Shape_896[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_895 ( [()] 0 tf_op_layer_Shape_895[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_897 ( [()] 0 tf_op_layer_Shape_897[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_899 ( [()] 0 tf_op_layer_Shape_899[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_898 ( [()] 0 tf_op_layer_Shape_898[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_534/shape ( [(3,)] 0 tf_op_layer_strided_slice_891[0][
tf_op_layer_strided_slice_893[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_535/shape ( [(3,)] 0 tf_op_layer_strided_slice_892[0][
tf_op_layer_strided_slice_893[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_536/shape ( [(3,)] 0 tf_op_layer_strided_slice_894[0][
tf_op_layer_strided_slice_896[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_537/shape ( [(3,)] 0 tf_op_layer_strided_slice_895[0][
tf_op_layer_strided_slice_896[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_538/shape ( [(3,)] 0 tf_op_layer_strided_slice_897[0][
tf_op_layer_strided_slice_899[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_539/shape ( [(3,)] 0 tf_op_layer_strided_slice_898[0][
tf_op_layer_strided_slice_899[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_534 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_89[0][0]
tf_op_layer_Reshape_534/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_534/multiples [(3,)] 0 tf_op_layer_strided_slice_892[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_535 (Tensor [(1, None, None)] 0 tf_op_layer_Add_89[0][0]
tf_op_layer_Reshape_535/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_535/multiples [(3,)] 0 tf_op_layer_strided_slice_891[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_536 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_536/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_536/multiples [(3,)] 0 tf_op_layer_strided_slice_895[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_537 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_537/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_537/multiples [(3,)] 0 tf_op_layer_strided_slice_894[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_538 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_89[0][0]
tf_op_layer_Reshape_538/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_538/multiples [(3,)] 0 tf_op_layer_strided_slice_898[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_539 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_539/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_539/multiples [(3,)] 0 tf_op_layer_strided_slice_897[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_534 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_534[0][0]
tf_op_layer_Tile_534/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_535 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_535[0][0]
tf_op_layer_Tile_535/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_536 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_536[0][0]
tf_op_layer_Tile_536/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_537 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_537[0][0]
tf_op_layer_Tile_537/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_538 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_538[0][0]
tf_op_layer_Tile_538/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_539 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_539[0][0]
tf_op_layer_Tile_539/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_445 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_534[0][0]
tf_op_layer_Tile_535[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_446 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_536[0][0]
tf_op_layer_Tile_537[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_447 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_538[0][0]
tf_op_layer_Tile_539[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_356 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_445[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_357 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_446[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_358 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_447[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_623 (TensorFlo [(None, None)] 0 tf_op_layer_Square_356[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_624 (TensorFlo [(None, None)] 0 tf_op_layer_Square_357[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_625 (TensorFlo [(None, None)] 0 tf_op_layer_Square_358[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_267 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_623[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_267 (TensorFlo [()] 0 tf_op_layer_strided_slice_893[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_268 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_624[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_268 (TensorFlo [()] 0 tf_op_layer_strided_slice_896[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_269 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_625[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_269 (TensorFlo [()] 0 tf_op_layer_strided_slice_899[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_267 (Tensor [(None, None)] 0 tf_op_layer_Neg_267[0][0]
tf_op_layer_Cast_267[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_268 (Tensor [(None, None)] 0 tf_op_layer_Neg_268[0][0]
tf_op_layer_Cast_268[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_269 (Tensor [(None, None)] 0 tf_op_layer_Neg_269[0][0]
tf_op_layer_Cast_269[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_267 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_267[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_268 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_268[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_269 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_269[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_626 (TensorFlo [()] 0 tf_op_layer_Exp_267[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_627 (TensorFlo [()] 0 tf_op_layer_Exp_268[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_628 (TensorFlo [()] 0 tf_op_layer_Exp_269[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_449 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_178 (TensorFl [()] 0 tf_op_layer_Mean_626[0][0]
tf_op_layer_Mean_627[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_268 (TensorFlow [()] 0 tf_op_layer_Mean_628[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_359 (TensorF [(None, 97)] 0 tf_op_layer_Sub_449[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_448 (TensorFlow [()] 0 tf_op_layer_AddV2_178[0][0]
tf_op_layer_Mul_268[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_89 (TensorFlowO [(None,)] 0 tf_op_layer_Square_359[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_269 (TensorFlow [()] 0 tf_op_layer_Sub_448[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_179 (TensorFl [(None,)] 0 tf_op_layer_Sum_89[0][0]
tf_op_layer_Mul_269[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_629 (TensorFlo [()] 0 tf_op_layer_AddV2_179[0][0]
__________________________________________________________________________________________________
add_loss_89 (AddLoss) () 0 tf_op_layer_Mean_629[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.9647WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0924s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 0.8435 - val_loss: 0.4458
Epoch 2/100
218/218 [==============================] - 0s 983us/step - loss: 0.4059 - val_loss: 0.3811
Epoch 3/100
218/218 [==============================] - 0s 988us/step - loss: 0.3829 - val_loss: 0.3700
Epoch 4/100
218/218 [==============================] - 0s 983us/step - loss: 0.3751 - val_loss: 0.3666
Epoch 5/100
218/218 [==============================] - 0s 985us/step - loss: 0.3687 - val_loss: 0.3612
Epoch 6/100
218/218 [==============================] - 0s 980us/step - loss: 0.3675 - val_loss: 0.3578
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3637 - val_loss: 0.3568
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3621 - val_loss: 0.3584
Epoch 9/100
218/218 [==============================] - 0s 982us/step - loss: 0.3608 - val_loss: 0.3541
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3586 - val_loss: 0.3536
Epoch 11/100
218/218 [==============================] - 0s 962us/step - loss: 0.3563 - val_loss: 0.3536
Epoch 12/100
218/218 [==============================] - 0s 976us/step - loss: 0.3561 - val_loss: 0.3551
Epoch 13/100
218/218 [==============================] - 0s 984us/step - loss: 0.3547 - val_loss: 0.3500
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3525 - val_loss: 0.3494
Epoch 15/100
218/218 [==============================] - 0s 988us/step - loss: 0.3540 - val_loss: 0.3498
Epoch 16/100
218/218 [==============================] - 0s 987us/step - loss: 0.3534 - val_loss: 0.3499
Epoch 17/100
218/218 [==============================] - 0s 978us/step - loss: 0.3520 - val_loss: 0.3470
Epoch 18/100
218/218 [==============================] - 0s 982us/step - loss: 0.3520 - val_loss: 0.3510
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3509 - val_loss: 0.3462
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3510 - val_loss: 0.3457
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3525 - val_loss: 0.3457
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3499 - val_loss: 0.3467
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3443
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3502 - val_loss: 0.3453
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3462
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3450
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3458
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3466
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3484 - val_loss: 0.3467
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3461
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3467
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3451
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3449
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3440
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3422
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3413
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3450
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3421
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3408
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3424
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3396
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3429
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3418
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3396
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3428
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3419
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3407
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3418
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3418
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3413
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3410
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3417
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3438
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3444
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3430
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3413
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3436
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3392
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3436
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3418
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3403
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3410
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3417
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3407
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3416
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3402
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3403
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3421
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3389
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3397
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3394
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3395
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3408
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3412
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3426
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3405
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3432
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3424
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3390
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3377
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3414
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3390
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3398
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3386
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3417
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3398
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3392
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3383
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3432
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3379
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3416
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3401
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3401
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3388
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3382
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3408
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3386
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3360
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3402
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3401
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_274 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_900 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_900 ( [()] 0 tf_op_layer_Shape_900[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_90 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_900[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_90[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_270 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_90 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_270[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_901 (TensorFl [(2,)] 0 tf_op_layer_Add_90[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_903 (TensorFl [(2,)] 0 tf_op_layer_Add_90[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_902 (TensorFl [(2,)] 0 tf_op_layer_Add_90[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_904 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_906 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_905 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_907 (TensorFl [(2,)] 0 tf_op_layer_Add_90[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_909 (TensorFl [(2,)] 0 tf_op_layer_Add_90[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_908 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_901 ( [()] 0 tf_op_layer_Shape_901[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_903 ( [()] 0 tf_op_layer_Shape_903[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_902 ( [()] 0 tf_op_layer_Shape_902[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_904 ( [()] 0 tf_op_layer_Shape_904[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_906 ( [()] 0 tf_op_layer_Shape_906[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_905 ( [()] 0 tf_op_layer_Shape_905[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_907 ( [()] 0 tf_op_layer_Shape_907[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_909 ( [()] 0 tf_op_layer_Shape_909[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_908 ( [()] 0 tf_op_layer_Shape_908[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_540/shape ( [(3,)] 0 tf_op_layer_strided_slice_901[0][
tf_op_layer_strided_slice_903[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_541/shape ( [(3,)] 0 tf_op_layer_strided_slice_902[0][
tf_op_layer_strided_slice_903[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_542/shape ( [(3,)] 0 tf_op_layer_strided_slice_904[0][
tf_op_layer_strided_slice_906[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_543/shape ( [(3,)] 0 tf_op_layer_strided_slice_905[0][
tf_op_layer_strided_slice_906[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_544/shape ( [(3,)] 0 tf_op_layer_strided_slice_907[0][
tf_op_layer_strided_slice_909[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_545/shape ( [(3,)] 0 tf_op_layer_strided_slice_908[0][
tf_op_layer_strided_slice_909[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_540 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_90[0][0]
tf_op_layer_Reshape_540/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_540/multiples [(3,)] 0 tf_op_layer_strided_slice_902[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_541 (Tensor [(1, None, None)] 0 tf_op_layer_Add_90[0][0]
tf_op_layer_Reshape_541/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_541/multiples [(3,)] 0 tf_op_layer_strided_slice_901[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_542 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_542/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_542/multiples [(3,)] 0 tf_op_layer_strided_slice_905[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_543 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_543/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_543/multiples [(3,)] 0 tf_op_layer_strided_slice_904[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_544 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_90[0][0]
tf_op_layer_Reshape_544/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_544/multiples [(3,)] 0 tf_op_layer_strided_slice_908[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_545 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_545/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_545/multiples [(3,)] 0 tf_op_layer_strided_slice_907[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_540 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_540[0][0]
tf_op_layer_Tile_540/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_541 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_541[0][0]
tf_op_layer_Tile_541/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_542 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_542[0][0]
tf_op_layer_Tile_542/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_543 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_543[0][0]
tf_op_layer_Tile_543/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_544 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_544[0][0]
tf_op_layer_Tile_544/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_545 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_545[0][0]
tf_op_layer_Tile_545/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_450 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_540[0][0]
tf_op_layer_Tile_541[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_451 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_542[0][0]
tf_op_layer_Tile_543[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_452 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_544[0][0]
tf_op_layer_Tile_545[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_360 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_450[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_361 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_451[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_362 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_452[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_630 (TensorFlo [(None, None)] 0 tf_op_layer_Square_360[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_631 (TensorFlo [(None, None)] 0 tf_op_layer_Square_361[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_632 (TensorFlo [(None, None)] 0 tf_op_layer_Square_362[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_270 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_630[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_270 (TensorFlo [()] 0 tf_op_layer_strided_slice_903[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_271 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_631[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_271 (TensorFlo [()] 0 tf_op_layer_strided_slice_906[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_272 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_632[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_272 (TensorFlo [()] 0 tf_op_layer_strided_slice_909[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_270 (Tensor [(None, None)] 0 tf_op_layer_Neg_270[0][0]
tf_op_layer_Cast_270[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_271 (Tensor [(None, None)] 0 tf_op_layer_Neg_271[0][0]
tf_op_layer_Cast_271[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_272 (Tensor [(None, None)] 0 tf_op_layer_Neg_272[0][0]
tf_op_layer_Cast_272[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_270 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_270[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_271 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_271[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_272 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_272[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_633 (TensorFlo [()] 0 tf_op_layer_Exp_270[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_634 (TensorFlo [()] 0 tf_op_layer_Exp_271[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_635 (TensorFlo [()] 0 tf_op_layer_Exp_272[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_454 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_180 (TensorFl [()] 0 tf_op_layer_Mean_633[0][0]
tf_op_layer_Mean_634[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_271 (TensorFlow [()] 0 tf_op_layer_Mean_635[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_363 (TensorF [(None, 97)] 0 tf_op_layer_Sub_454[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_453 (TensorFlow [()] 0 tf_op_layer_AddV2_180[0][0]
tf_op_layer_Mul_271[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_90 (TensorFlowO [(None,)] 0 tf_op_layer_Square_363[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_272 (TensorFlow [()] 0 tf_op_layer_Sub_453[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_181 (TensorFl [(None,)] 0 tf_op_layer_Sum_90[0][0]
tf_op_layer_Mul_272[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_636 (TensorFlo [()] 0 tf_op_layer_AddV2_181[0][0]
__________________________________________________________________________________________________
add_loss_90 (AddLoss) () 0 tf_op_layer_Mean_636[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 7.9944WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.1096s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 2.0315 - val_loss: 1.0301
Epoch 2/100
218/218 [==============================] - 0s 928us/step - loss: 0.9000 - val_loss: 0.7846
Epoch 3/100
218/218 [==============================] - 0s 924us/step - loss: 0.7268 - val_loss: 0.6663
Epoch 4/100
218/218 [==============================] - 0s 939us/step - loss: 0.6495 - val_loss: 0.6136
Epoch 5/100
218/218 [==============================] - 0s 935us/step - loss: 0.6126 - val_loss: 0.5897
Epoch 6/100
218/218 [==============================] - 0s 933us/step - loss: 0.5876 - val_loss: 0.5646
Epoch 7/100
218/218 [==============================] - 0s 940us/step - loss: 0.5719 - val_loss: 0.5554
Epoch 8/100
218/218 [==============================] - 0s 945us/step - loss: 0.5575 - val_loss: 0.5448
Epoch 9/100
218/218 [==============================] - 0s 931us/step - loss: 0.5492 - val_loss: 0.5388
Epoch 10/100
218/218 [==============================] - 0s 929us/step - loss: 0.5384 - val_loss: 0.5265
Epoch 11/100
218/218 [==============================] - 0s 930us/step - loss: 0.5336 - val_loss: 0.5203
Epoch 12/100
218/218 [==============================] - 0s 936us/step - loss: 0.5258 - val_loss: 0.5091
Epoch 13/100
218/218 [==============================] - 0s 933us/step - loss: 0.5217 - val_loss: 0.5044
Epoch 14/100
218/218 [==============================] - 0s 935us/step - loss: 0.5176 - val_loss: 0.5042
Epoch 15/100
218/218 [==============================] - 0s 946us/step - loss: 0.5122 - val_loss: 0.5025
Epoch 16/100
218/218 [==============================] - 0s 938us/step - loss: 0.5094 - val_loss: 0.4973
Epoch 17/100
218/218 [==============================] - 0s 934us/step - loss: 0.5069 - val_loss: 0.4943
Epoch 18/100
218/218 [==============================] - 0s 940us/step - loss: 0.5036 - val_loss: 0.4918
Epoch 19/100
218/218 [==============================] - 0s 931us/step - loss: 0.5006 - val_loss: 0.4870
Epoch 20/100
218/218 [==============================] - 0s 935us/step - loss: 0.4963 - val_loss: 0.4835
Epoch 21/100
218/218 [==============================] - 0s 902us/step - loss: 0.4989 - val_loss: 0.4825
Epoch 22/100
218/218 [==============================] - 0s 904us/step - loss: 0.4958 - val_loss: 0.4829
Epoch 23/100
218/218 [==============================] - 0s 943us/step - loss: 0.4927 - val_loss: 0.4744
Epoch 24/100
218/218 [==============================] - 0s 930us/step - loss: 0.4913 - val_loss: 0.4768
Epoch 25/100
218/218 [==============================] - 0s 936us/step - loss: 0.4878 - val_loss: 0.4793
Epoch 26/100
218/218 [==============================] - 0s 929us/step - loss: 0.4861 - val_loss: 0.4767
Epoch 27/100
218/218 [==============================] - 0s 924us/step - loss: 0.4875 - val_loss: 0.4697
Epoch 28/100
218/218 [==============================] - 0s 928us/step - loss: 0.4869 - val_loss: 0.4718
Epoch 29/100
218/218 [==============================] - 0s 984us/step - loss: 0.4850 - val_loss: 0.4738
Epoch 30/100
218/218 [==============================] - 0s 984us/step - loss: 0.4839 - val_loss: 0.4725
Epoch 31/100
218/218 [==============================] - 0s 945us/step - loss: 0.4824 - val_loss: 0.4667
Epoch 32/100
218/218 [==============================] - 0s 952us/step - loss: 0.4803 - val_loss: 0.4705
Epoch 33/100
218/218 [==============================] - 0s 943us/step - loss: 0.4805 - val_loss: 0.4686
Epoch 34/100
218/218 [==============================] - 0s 932us/step - loss: 0.4799 - val_loss: 0.4678
Epoch 35/100
218/218 [==============================] - 0s 937us/step - loss: 0.4786 - val_loss: 0.4663
Epoch 36/100
218/218 [==============================] - 0s 930us/step - loss: 0.4762 - val_loss: 0.4668
Epoch 37/100
218/218 [==============================] - 0s 935us/step - loss: 0.4786 - val_loss: 0.4628
Epoch 38/100
218/218 [==============================] - 0s 950us/step - loss: 0.4759 - val_loss: 0.4626
Epoch 39/100
218/218 [==============================] - 0s 943us/step - loss: 0.4764 - val_loss: 0.4616
Epoch 40/100
218/218 [==============================] - 0s 939us/step - loss: 0.4752 - val_loss: 0.4637
Epoch 41/100
218/218 [==============================] - 0s 933us/step - loss: 0.4746 - val_loss: 0.4641
Epoch 42/100
218/218 [==============================] - 0s 936us/step - loss: 0.4764 - val_loss: 0.4589
Epoch 43/100
218/218 [==============================] - 0s 940us/step - loss: 0.4747 - val_loss: 0.4647
Epoch 44/100
218/218 [==============================] - 0s 938us/step - loss: 0.4750 - val_loss: 0.4585
Epoch 45/100
218/218 [==============================] - 0s 931us/step - loss: 0.4727 - val_loss: 0.4609
Epoch 46/100
218/218 [==============================] - 0s 937us/step - loss: 0.4727 - val_loss: 0.4581
Epoch 47/100
218/218 [==============================] - 0s 936us/step - loss: 0.4713 - val_loss: 0.4586
Epoch 48/100
218/218 [==============================] - 0s 937us/step - loss: 0.4728 - val_loss: 0.4596
Epoch 49/100
218/218 [==============================] - 0s 934us/step - loss: 0.4713 - val_loss: 0.4570
Epoch 50/100
218/218 [==============================] - 0s 951us/step - loss: 0.4693 - val_loss: 0.4525
Epoch 51/100
218/218 [==============================] - 0s 941us/step - loss: 0.4709 - val_loss: 0.4565
Epoch 52/100
218/218 [==============================] - 0s 942us/step - loss: 0.4687 - val_loss: 0.4586
Epoch 53/100
218/218 [==============================] - 0s 933us/step - loss: 0.4675 - val_loss: 0.4574
Epoch 54/100
218/218 [==============================] - 0s 937us/step - loss: 0.4691 - val_loss: 0.4584
Epoch 55/100
218/218 [==============================] - 0s 937us/step - loss: 0.4695 - val_loss: 0.4554
Epoch 56/100
218/218 [==============================] - 0s 934us/step - loss: 0.4677 - val_loss: 0.4591
Epoch 57/100
218/218 [==============================] - 0s 939us/step - loss: 0.4667 - val_loss: 0.4565
Epoch 58/100
218/218 [==============================] - 0s 937us/step - loss: 0.4685 - val_loss: 0.4549
Epoch 59/100
218/218 [==============================] - 0s 934us/step - loss: 0.4670 - val_loss: 0.4547
Epoch 60/100
218/218 [==============================] - 0s 933us/step - loss: 0.4677 - val_loss: 0.4491
Epoch 61/100
218/218 [==============================] - 0s 937us/step - loss: 0.4671 - val_loss: 0.4568
Epoch 62/100
218/218 [==============================] - 0s 941us/step - loss: 0.4666 - val_loss: 0.4540
Epoch 63/100
218/218 [==============================] - 0s 935us/step - loss: 0.4654 - val_loss: 0.4506
Epoch 64/100
218/218 [==============================] - 0s 939us/step - loss: 0.4656 - val_loss: 0.4554
Epoch 65/100
218/218 [==============================] - 0s 950us/step - loss: 0.4662 - val_loss: 0.4549
Epoch 66/100
218/218 [==============================] - 0s 944us/step - loss: 0.4656 - val_loss: 0.4554
Epoch 67/100
218/218 [==============================] - 0s 942us/step - loss: 0.4662 - val_loss: 0.4503
Epoch 68/100
218/218 [==============================] - 0s 935us/step - loss: 0.4648 - val_loss: 0.4520
Epoch 69/100
218/218 [==============================] - 0s 995us/step - loss: 0.4630 - val_loss: 0.4530
Epoch 70/100
218/218 [==============================] - 0s 948us/step - loss: 0.4634 - val_loss: 0.4527
Epoch 71/100
218/218 [==============================] - 0s 952us/step - loss: 0.4632 - val_loss: 0.4515
Epoch 72/100
218/218 [==============================] - 0s 999us/step - loss: 0.4630 - val_loss: 0.4547
Epoch 73/100
218/218 [==============================] - 0s 937us/step - loss: 0.4653 - val_loss: 0.4505
Epoch 74/100
218/218 [==============================] - 0s 933us/step - loss: 0.4642 - val_loss: 0.4495
Epoch 75/100
218/218 [==============================] - 0s 934us/step - loss: 0.4639 - val_loss: 0.4499
Epoch 76/100
218/218 [==============================] - 0s 937us/step - loss: 0.4607 - val_loss: 0.4544
Epoch 77/100
218/218 [==============================] - 0s 936us/step - loss: 0.4635 - val_loss: 0.4480
Epoch 78/100
218/218 [==============================] - 0s 930us/step - loss: 0.4622 - val_loss: 0.4507
Epoch 79/100
218/218 [==============================] - 0s 938us/step - loss: 0.4627 - val_loss: 0.4495
Epoch 80/100
218/218 [==============================] - 0s 946us/step - loss: 0.4599 - val_loss: 0.4485
Epoch 81/100
218/218 [==============================] - 0s 942us/step - loss: 0.4622 - val_loss: 0.4478
Epoch 82/100
218/218 [==============================] - 0s 933us/step - loss: 0.4620 - val_loss: 0.4466
Epoch 83/100
218/218 [==============================] - 0s 934us/step - loss: 0.4626 - val_loss: 0.4503
Epoch 84/100
218/218 [==============================] - 0s 930us/step - loss: 0.4621 - val_loss: 0.4487
Epoch 85/100
218/218 [==============================] - 0s 936us/step - loss: 0.4613 - val_loss: 0.4474
Epoch 86/100
218/218 [==============================] - 0s 931us/step - loss: 0.4612 - val_loss: 0.4480
Epoch 87/100
218/218 [==============================] - 0s 930us/step - loss: 0.4609 - val_loss: 0.4464
Epoch 88/100
218/218 [==============================] - 0s 969us/step - loss: 0.4620 - val_loss: 0.4455
Epoch 89/100
218/218 [==============================] - 0s 942us/step - loss: 0.4610 - val_loss: 0.4462
Epoch 90/100
218/218 [==============================] - 0s 945us/step - loss: 0.4599 - val_loss: 0.4476
Epoch 91/100
218/218 [==============================] - 0s 952us/step - loss: 0.4603 - val_loss: 0.4489
Epoch 92/100
218/218 [==============================] - 0s 947us/step - loss: 0.4603 - val_loss: 0.4510
Epoch 93/100
218/218 [==============================] - 0s 950us/step - loss: 0.4618 - val_loss: 0.4503
Epoch 94/100
218/218 [==============================] - 0s 945us/step - loss: 0.4607 - val_loss: 0.4484
Epoch 95/100
218/218 [==============================] - 0s 956us/step - loss: 0.4613 - val_loss: 0.4480
Epoch 96/100
218/218 [==============================] - 0s 974us/step - loss: 0.4598 - val_loss: 0.4486
Epoch 97/100
218/218 [==============================] - 0s 971us/step - loss: 0.4604 - val_loss: 0.4479
Epoch 98/100
218/218 [==============================] - 0s 970us/step - loss: 0.4601 - val_loss: 0.4496
Epoch 99/100
218/218 [==============================] - 0s 969us/step - loss: 0.4590 - val_loss: 0.4493
Epoch 100/100
218/218 [==============================] - 0s 984us/step - loss: 0.4610 - val_loss: 0.4447
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.23 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.49 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.85 seconds.
Calculating KMeans...
Calculated KMeans in 18.03 seconds.
Calculated landmark operator in 20.34 seconds.
Calculating optimal t...
Automatically selected t = 26
Calculated optimal t in 2.86 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.54 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.34 seconds.
Calculated PHATE in 53.59 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_275 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_276 (Dense) (None, 32) 2080 dense_275[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_276[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_276[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_277 (Dense) (None, 32) 128
_________________________________________________________________
dense_278 (Dense) (None, 64) 2112
_________________________________________________________________
dense_279 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_275 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_276 (Dense) (None, 32) 2080 dense_275[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_276[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_276[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_910 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_910 ( [()] 0 tf_op_layer_Shape_910[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_91 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_910[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_91[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_273 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_91 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_273[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_911 (TensorFl [(2,)] 0 tf_op_layer_Add_91[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_913 (TensorFl [(2,)] 0 tf_op_layer_Add_91[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_912 (TensorFl [(2,)] 0 tf_op_layer_Add_91[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_914 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_916 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_915 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_917 (TensorFl [(2,)] 0 tf_op_layer_Add_91[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_919 (TensorFl [(2,)] 0 tf_op_layer_Add_91[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_918 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_911 ( [()] 0 tf_op_layer_Shape_911[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_913 ( [()] 0 tf_op_layer_Shape_913[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_912 ( [()] 0 tf_op_layer_Shape_912[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_914 ( [()] 0 tf_op_layer_Shape_914[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_916 ( [()] 0 tf_op_layer_Shape_916[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_915 ( [()] 0 tf_op_layer_Shape_915[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_917 ( [()] 0 tf_op_layer_Shape_917[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_919 ( [()] 0 tf_op_layer_Shape_919[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_918 ( [()] 0 tf_op_layer_Shape_918[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_546/shape ( [(3,)] 0 tf_op_layer_strided_slice_911[0][
tf_op_layer_strided_slice_913[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_547/shape ( [(3,)] 0 tf_op_layer_strided_slice_912[0][
tf_op_layer_strided_slice_913[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_548/shape ( [(3,)] 0 tf_op_layer_strided_slice_914[0][
tf_op_layer_strided_slice_916[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_549/shape ( [(3,)] 0 tf_op_layer_strided_slice_915[0][
tf_op_layer_strided_slice_916[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_550/shape ( [(3,)] 0 tf_op_layer_strided_slice_917[0][
tf_op_layer_strided_slice_919[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_551/shape ( [(3,)] 0 tf_op_layer_strided_slice_918[0][
tf_op_layer_strided_slice_919[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_546 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_91[0][0]
tf_op_layer_Reshape_546/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_546/multiples [(3,)] 0 tf_op_layer_strided_slice_912[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_547 (Tensor [(1, None, None)] 0 tf_op_layer_Add_91[0][0]
tf_op_layer_Reshape_547/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_547/multiples [(3,)] 0 tf_op_layer_strided_slice_911[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_548 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_548/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_548/multiples [(3,)] 0 tf_op_layer_strided_slice_915[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_549 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_549/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_549/multiples [(3,)] 0 tf_op_layer_strided_slice_914[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_550 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_91[0][0]
tf_op_layer_Reshape_550/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_550/multiples [(3,)] 0 tf_op_layer_strided_slice_918[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_551 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_551/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_551/multiples [(3,)] 0 tf_op_layer_strided_slice_917[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_546 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_546[0][0]
tf_op_layer_Tile_546/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_547 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_547[0][0]
tf_op_layer_Tile_547/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_548 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_548[0][0]
tf_op_layer_Tile_548/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_549 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_549[0][0]
tf_op_layer_Tile_549/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_550 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_550[0][0]
tf_op_layer_Tile_550/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_551 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_551[0][0]
tf_op_layer_Tile_551/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_455 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_546[0][0]
tf_op_layer_Tile_547[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_456 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_548[0][0]
tf_op_layer_Tile_549[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_457 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_550[0][0]
tf_op_layer_Tile_551[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_364 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_455[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_365 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_456[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_366 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_457[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_637 (TensorFlo [(None, None)] 0 tf_op_layer_Square_364[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_638 (TensorFlo [(None, None)] 0 tf_op_layer_Square_365[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_639 (TensorFlo [(None, None)] 0 tf_op_layer_Square_366[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_273 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_637[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_273 (TensorFlo [()] 0 tf_op_layer_strided_slice_913[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_274 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_638[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_274 (TensorFlo [()] 0 tf_op_layer_strided_slice_916[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_275 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_639[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_275 (TensorFlo [()] 0 tf_op_layer_strided_slice_919[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_273 (Tensor [(None, None)] 0 tf_op_layer_Neg_273[0][0]
tf_op_layer_Cast_273[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_274 (Tensor [(None, None)] 0 tf_op_layer_Neg_274[0][0]
tf_op_layer_Cast_274[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_275 (Tensor [(None, None)] 0 tf_op_layer_Neg_275[0][0]
tf_op_layer_Cast_275[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_273 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_273[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_274 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_274[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_275 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_275[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_640 (TensorFlo [()] 0 tf_op_layer_Exp_273[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_641 (TensorFlo [()] 0 tf_op_layer_Exp_274[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_642 (TensorFlo [()] 0 tf_op_layer_Exp_275[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_459 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_182 (TensorFl [()] 0 tf_op_layer_Mean_640[0][0]
tf_op_layer_Mean_641[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_274 (TensorFlow [()] 0 tf_op_layer_Mean_642[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_367 (TensorF [(None, 97)] 0 tf_op_layer_Sub_459[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_458 (TensorFlow [()] 0 tf_op_layer_AddV2_182[0][0]
tf_op_layer_Mul_274[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_91 (TensorFlowO [(None,)] 0 tf_op_layer_Square_367[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_275 (TensorFlow [()] 0 tf_op_layer_Sub_458[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_183 (TensorFl [(None,)] 0 tf_op_layer_Sum_91[0][0]
tf_op_layer_Mul_275[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_643 (TensorFlo [()] 0 tf_op_layer_AddV2_183[0][0]
__________________________________________________________________________________________________
add_loss_91 (AddLoss) () 0 tf_op_layer_Mean_643[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.6975WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.0975s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.8501 - val_loss: 0.4351
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4098 - val_loss: 0.3811
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3849 - val_loss: 0.3672
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3775 - val_loss: 0.3618
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3726 - val_loss: 0.3602
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3689 - val_loss: 0.3618
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3639 - val_loss: 0.3563
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3636 - val_loss: 0.3537
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3602 - val_loss: 0.3506
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3591 - val_loss: 0.3510
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3581 - val_loss: 0.3472
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3576 - val_loss: 0.3494
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3574 - val_loss: 0.3443
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3551 - val_loss: 0.3462
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3538 - val_loss: 0.3463
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3548 - val_loss: 0.3463
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3531 - val_loss: 0.3460
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3522 - val_loss: 0.3427
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3519 - val_loss: 0.3425
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3533 - val_loss: 0.3417
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3420
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3508 - val_loss: 0.3431
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3502 - val_loss: 0.3438
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3403
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3407
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3440
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3499 - val_loss: 0.3439
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3486 - val_loss: 0.3383
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3421
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3482 - val_loss: 0.3399
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3399
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3394
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3393
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3486 - val_loss: 0.3416
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3394
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3483 - val_loss: 0.3381
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3391
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3399
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3401
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3363
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3381
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3391
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3346
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3346
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3398
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3455 - val_loss: 0.3380
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3392
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3380
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3398
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3368
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3368
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3388
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3385
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3348
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3345
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3366
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3364
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3375
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3342
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3361
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3373
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3363
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3353
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3361
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3406
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3365
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3374
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3377
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3361
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3355
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3367
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3355
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3363
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3361
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3357
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3354
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3375
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3367
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3368
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3348
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3373
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3345
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3339
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3361
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3392
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3340
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3352
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3395
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3354
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3352
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3354
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3342
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3338
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3321
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3392 - val_loss: 0.3364
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3358
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3353
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3330
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3333
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3331
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_280 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_920 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_920 ( [()] 0 tf_op_layer_Shape_920[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_92 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_920[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_92[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_276 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_92 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_276[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_921 (TensorFl [(2,)] 0 tf_op_layer_Add_92[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_923 (TensorFl [(2,)] 0 tf_op_layer_Add_92[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_922 (TensorFl [(2,)] 0 tf_op_layer_Add_92[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_924 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_926 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_925 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_927 (TensorFl [(2,)] 0 tf_op_layer_Add_92[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_929 (TensorFl [(2,)] 0 tf_op_layer_Add_92[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_928 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_921 ( [()] 0 tf_op_layer_Shape_921[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_923 ( [()] 0 tf_op_layer_Shape_923[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_922 ( [()] 0 tf_op_layer_Shape_922[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_924 ( [()] 0 tf_op_layer_Shape_924[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_926 ( [()] 0 tf_op_layer_Shape_926[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_925 ( [()] 0 tf_op_layer_Shape_925[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_927 ( [()] 0 tf_op_layer_Shape_927[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_929 ( [()] 0 tf_op_layer_Shape_929[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_928 ( [()] 0 tf_op_layer_Shape_928[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_552/shape ( [(3,)] 0 tf_op_layer_strided_slice_921[0][
tf_op_layer_strided_slice_923[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_553/shape ( [(3,)] 0 tf_op_layer_strided_slice_922[0][
tf_op_layer_strided_slice_923[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_554/shape ( [(3,)] 0 tf_op_layer_strided_slice_924[0][
tf_op_layer_strided_slice_926[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_555/shape ( [(3,)] 0 tf_op_layer_strided_slice_925[0][
tf_op_layer_strided_slice_926[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_556/shape ( [(3,)] 0 tf_op_layer_strided_slice_927[0][
tf_op_layer_strided_slice_929[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_557/shape ( [(3,)] 0 tf_op_layer_strided_slice_928[0][
tf_op_layer_strided_slice_929[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_552 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_92[0][0]
tf_op_layer_Reshape_552/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_552/multiples [(3,)] 0 tf_op_layer_strided_slice_922[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_553 (Tensor [(1, None, None)] 0 tf_op_layer_Add_92[0][0]
tf_op_layer_Reshape_553/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_553/multiples [(3,)] 0 tf_op_layer_strided_slice_921[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_554 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_554/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_554/multiples [(3,)] 0 tf_op_layer_strided_slice_925[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_555 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_555/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_555/multiples [(3,)] 0 tf_op_layer_strided_slice_924[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_556 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_92[0][0]
tf_op_layer_Reshape_556/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_556/multiples [(3,)] 0 tf_op_layer_strided_slice_928[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_557 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_557/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_557/multiples [(3,)] 0 tf_op_layer_strided_slice_927[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_552 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_552[0][0]
tf_op_layer_Tile_552/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_553 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_553[0][0]
tf_op_layer_Tile_553/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_554 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_554[0][0]
tf_op_layer_Tile_554/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_555 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_555[0][0]
tf_op_layer_Tile_555/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_556 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_556[0][0]
tf_op_layer_Tile_556/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_557 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_557[0][0]
tf_op_layer_Tile_557/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_460 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_552[0][0]
tf_op_layer_Tile_553[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_461 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_554[0][0]
tf_op_layer_Tile_555[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_462 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_556[0][0]
tf_op_layer_Tile_557[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_368 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_460[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_369 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_461[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_370 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_462[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_644 (TensorFlo [(None, None)] 0 tf_op_layer_Square_368[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_645 (TensorFlo [(None, None)] 0 tf_op_layer_Square_369[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_646 (TensorFlo [(None, None)] 0 tf_op_layer_Square_370[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_276 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_644[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_276 (TensorFlo [()] 0 tf_op_layer_strided_slice_923[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_277 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_645[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_277 (TensorFlo [()] 0 tf_op_layer_strided_slice_926[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_278 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_646[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_278 (TensorFlo [()] 0 tf_op_layer_strided_slice_929[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_276 (Tensor [(None, None)] 0 tf_op_layer_Neg_276[0][0]
tf_op_layer_Cast_276[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_277 (Tensor [(None, None)] 0 tf_op_layer_Neg_277[0][0]
tf_op_layer_Cast_277[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_278 (Tensor [(None, None)] 0 tf_op_layer_Neg_278[0][0]
tf_op_layer_Cast_278[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_276 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_276[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_277 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_277[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_278 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_278[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_647 (TensorFlo [()] 0 tf_op_layer_Exp_276[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_648 (TensorFlo [()] 0 tf_op_layer_Exp_277[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_649 (TensorFlo [()] 0 tf_op_layer_Exp_278[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_464 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_184 (TensorFl [()] 0 tf_op_layer_Mean_647[0][0]
tf_op_layer_Mean_648[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_277 (TensorFlow [()] 0 tf_op_layer_Mean_649[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_371 (TensorF [(None, 97)] 0 tf_op_layer_Sub_464[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_463 (TensorFlow [()] 0 tf_op_layer_AddV2_184[0][0]
tf_op_layer_Mul_277[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_92 (TensorFlowO [(None,)] 0 tf_op_layer_Square_371[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_278 (TensorFlow [()] 0 tf_op_layer_Sub_463[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_185 (TensorFl [(None,)] 0 tf_op_layer_Sum_92[0][0]
tf_op_layer_Mul_278[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_650 (TensorFlo [()] 0 tf_op_layer_AddV2_185[0][0]
__________________________________________________________________________________________________
add_loss_92 (AddLoss) () 0 tf_op_layer_Mean_650[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.3974WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0935s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9913 - val_loss: 0.9650
Epoch 2/100
218/218 [==============================] - 0s 963us/step - loss: 0.8577 - val_loss: 0.7901
Epoch 3/100
218/218 [==============================] - 0s 960us/step - loss: 0.7260 - val_loss: 0.6837
Epoch 4/100
218/218 [==============================] - 0s 959us/step - loss: 0.6583 - val_loss: 0.6403
Epoch 5/100
218/218 [==============================] - 0s 962us/step - loss: 0.6197 - val_loss: 0.6093
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5916 - val_loss: 0.5878
Epoch 7/100
218/218 [==============================] - 0s 994us/step - loss: 0.5729 - val_loss: 0.5798
Epoch 8/100
218/218 [==============================] - 0s 970us/step - loss: 0.5620 - val_loss: 0.5581
Epoch 9/100
218/218 [==============================] - 0s 973us/step - loss: 0.5546 - val_loss: 0.5555
Epoch 10/100
218/218 [==============================] - 0s 966us/step - loss: 0.5436 - val_loss: 0.5464
Epoch 11/100
218/218 [==============================] - 0s 967us/step - loss: 0.5389 - val_loss: 0.5433
Epoch 12/100
218/218 [==============================] - 0s 981us/step - loss: 0.5327 - val_loss: 0.5451
Epoch 13/100
218/218 [==============================] - 0s 968us/step - loss: 0.5283 - val_loss: 0.5321
Epoch 14/100
218/218 [==============================] - 0s 966us/step - loss: 0.5207 - val_loss: 0.5285
Epoch 15/100
218/218 [==============================] - 0s 978us/step - loss: 0.5170 - val_loss: 0.5235
Epoch 16/100
218/218 [==============================] - 0s 971us/step - loss: 0.5157 - val_loss: 0.5206
Epoch 17/100
218/218 [==============================] - 0s 979us/step - loss: 0.5123 - val_loss: 0.5124
Epoch 18/100
218/218 [==============================] - 0s 965us/step - loss: 0.5109 - val_loss: 0.5183
Epoch 19/100
218/218 [==============================] - 0s 970us/step - loss: 0.5094 - val_loss: 0.5157
Epoch 20/100
218/218 [==============================] - 0s 981us/step - loss: 0.5005 - val_loss: 0.5123
Epoch 21/100
218/218 [==============================] - 0s 997us/step - loss: 0.5014 - val_loss: 0.5065
Epoch 22/100
218/218 [==============================] - 0s 964us/step - loss: 0.4999 - val_loss: 0.5118
Epoch 23/100
218/218 [==============================] - 0s 962us/step - loss: 0.4997 - val_loss: 0.5047
Epoch 24/100
218/218 [==============================] - 0s 956us/step - loss: 0.4952 - val_loss: 0.5026
Epoch 25/100
218/218 [==============================] - 0s 957us/step - loss: 0.4962 - val_loss: 0.5013
Epoch 26/100
218/218 [==============================] - 0s 965us/step - loss: 0.4939 - val_loss: 0.5001
Epoch 27/100
218/218 [==============================] - 0s 968us/step - loss: 0.4918 - val_loss: 0.5009
Epoch 28/100
218/218 [==============================] - 0s 960us/step - loss: 0.4928 - val_loss: 0.5000
Epoch 29/100
218/218 [==============================] - 0s 960us/step - loss: 0.4899 - val_loss: 0.4991
Epoch 30/100
218/218 [==============================] - 0s 965us/step - loss: 0.4890 - val_loss: 0.4972
Epoch 31/100
218/218 [==============================] - 0s 956us/step - loss: 0.4879 - val_loss: 0.4947
Epoch 32/100
218/218 [==============================] - 0s 969us/step - loss: 0.4865 - val_loss: 0.4937
Epoch 33/100
218/218 [==============================] - 0s 968us/step - loss: 0.4829 - val_loss: 0.4966
Epoch 34/100
218/218 [==============================] - 0s 962us/step - loss: 0.4859 - val_loss: 0.4955
Epoch 35/100
218/218 [==============================] - 0s 960us/step - loss: 0.4841 - val_loss: 0.4926
Epoch 36/100
218/218 [==============================] - 0s 955us/step - loss: 0.4832 - val_loss: 0.4907
Epoch 37/100
218/218 [==============================] - 0s 954us/step - loss: 0.4821 - val_loss: 0.4885
Epoch 38/100
218/218 [==============================] - 0s 952us/step - loss: 0.4815 - val_loss: 0.4901
Epoch 39/100
218/218 [==============================] - 0s 957us/step - loss: 0.4793 - val_loss: 0.4864
Epoch 40/100
218/218 [==============================] - 0s 978us/step - loss: 0.4766 - val_loss: 0.4868
Epoch 41/100
218/218 [==============================] - 0s 963us/step - loss: 0.4790 - val_loss: 0.4879
Epoch 42/100
218/218 [==============================] - 0s 962us/step - loss: 0.4775 - val_loss: 0.4882
Epoch 43/100
218/218 [==============================] - 0s 960us/step - loss: 0.4763 - val_loss: 0.4870
Epoch 44/100
218/218 [==============================] - 0s 964us/step - loss: 0.4750 - val_loss: 0.4843
Epoch 45/100
218/218 [==============================] - 0s 966us/step - loss: 0.4794 - val_loss: 0.4899
Epoch 46/100
218/218 [==============================] - 0s 973us/step - loss: 0.4767 - val_loss: 0.4811
Epoch 47/100
218/218 [==============================] - 0s 960us/step - loss: 0.4768 - val_loss: 0.4856
Epoch 48/100
218/218 [==============================] - 0s 964us/step - loss: 0.4747 - val_loss: 0.4868
Epoch 49/100
218/218 [==============================] - 0s 966us/step - loss: 0.4772 - val_loss: 0.4864
Epoch 50/100
218/218 [==============================] - 0s 961us/step - loss: 0.4734 - val_loss: 0.4868
Epoch 51/100
218/218 [==============================] - 0s 963us/step - loss: 0.4742 - val_loss: 0.4832
Epoch 52/100
218/218 [==============================] - 0s 959us/step - loss: 0.4731 - val_loss: 0.4812
Epoch 53/100
218/218 [==============================] - 0s 959us/step - loss: 0.4723 - val_loss: 0.4855
Epoch 54/100
218/218 [==============================] - 0s 975us/step - loss: 0.4733 - val_loss: 0.4836
Epoch 55/100
218/218 [==============================] - 0s 979us/step - loss: 0.4735 - val_loss: 0.4808
Epoch 56/100
218/218 [==============================] - 0s 968us/step - loss: 0.4739 - val_loss: 0.4850
Epoch 57/100
218/218 [==============================] - 0s 965us/step - loss: 0.4732 - val_loss: 0.4813
Epoch 58/100
218/218 [==============================] - 0s 963us/step - loss: 0.4711 - val_loss: 0.4786
Epoch 59/100
218/218 [==============================] - 0s 966us/step - loss: 0.4727 - val_loss: 0.4799
Epoch 60/100
218/218 [==============================] - 0s 962us/step - loss: 0.4727 - val_loss: 0.4813
Epoch 61/100
218/218 [==============================] - 0s 967us/step - loss: 0.4703 - val_loss: 0.4796
Epoch 62/100
218/218 [==============================] - 0s 959us/step - loss: 0.4704 - val_loss: 0.4789
Epoch 63/100
218/218 [==============================] - 0s 967us/step - loss: 0.4701 - val_loss: 0.4808
Epoch 64/100
218/218 [==============================] - 0s 970us/step - loss: 0.4693 - val_loss: 0.4780
Epoch 65/100
218/218 [==============================] - 0s 967us/step - loss: 0.4717 - val_loss: 0.4806
Epoch 66/100
218/218 [==============================] - 0s 962us/step - loss: 0.4681 - val_loss: 0.4763
Epoch 67/100
218/218 [==============================] - 0s 963us/step - loss: 0.4693 - val_loss: 0.4754
Epoch 68/100
218/218 [==============================] - 0s 965us/step - loss: 0.4685 - val_loss: 0.4795
Epoch 69/100
218/218 [==============================] - 0s 966us/step - loss: 0.4696 - val_loss: 0.4762
Epoch 70/100
218/218 [==============================] - 0s 972us/step - loss: 0.4675 - val_loss: 0.4740
Epoch 71/100
218/218 [==============================] - 0s 957us/step - loss: 0.4695 - val_loss: 0.4762
Epoch 72/100
218/218 [==============================] - 0s 963us/step - loss: 0.4691 - val_loss: 0.4821
Epoch 73/100
218/218 [==============================] - 0s 967us/step - loss: 0.4680 - val_loss: 0.4772
Epoch 74/100
218/218 [==============================] - 0s 960us/step - loss: 0.4691 - val_loss: 0.4776
Epoch 75/100
218/218 [==============================] - 0s 966us/step - loss: 0.4677 - val_loss: 0.4765
Epoch 76/100
218/218 [==============================] - 0s 976us/step - loss: 0.4662 - val_loss: 0.4752
Epoch 77/100
218/218 [==============================] - 0s 976us/step - loss: 0.4674 - val_loss: 0.4776
Epoch 78/100
218/218 [==============================] - 0s 963us/step - loss: 0.4667 - val_loss: 0.4767
Epoch 79/100
218/218 [==============================] - 0s 971us/step - loss: 0.4675 - val_loss: 0.4754
Epoch 80/100
218/218 [==============================] - 0s 963us/step - loss: 0.4642 - val_loss: 0.4755
Epoch 81/100
218/218 [==============================] - 0s 964us/step - loss: 0.4644 - val_loss: 0.4782
Epoch 82/100
218/218 [==============================] - 0s 971us/step - loss: 0.4653 - val_loss: 0.4732
Epoch 83/100
218/218 [==============================] - 0s 960us/step - loss: 0.4656 - val_loss: 0.4795
Epoch 84/100
218/218 [==============================] - 0s 964us/step - loss: 0.4655 - val_loss: 0.4750
Epoch 85/100
218/218 [==============================] - 0s 974us/step - loss: 0.4647 - val_loss: 0.4727
Epoch 86/100
218/218 [==============================] - 0s 969us/step - loss: 0.4634 - val_loss: 0.4740
Epoch 87/100
218/218 [==============================] - 0s 963us/step - loss: 0.4653 - val_loss: 0.4762
Epoch 88/100
218/218 [==============================] - 0s 960us/step - loss: 0.4656 - val_loss: 0.4717
Epoch 89/100
218/218 [==============================] - 0s 975us/step - loss: 0.4636 - val_loss: 0.4752
Epoch 90/100
218/218 [==============================] - 0s 960us/step - loss: 0.4632 - val_loss: 0.4720
Epoch 91/100
218/218 [==============================] - 0s 962us/step - loss: 0.4638 - val_loss: 0.4697
Epoch 92/100
218/218 [==============================] - 0s 969us/step - loss: 0.4638 - val_loss: 0.4700
Epoch 93/100
218/218 [==============================] - 0s 981us/step - loss: 0.4624 - val_loss: 0.4757
Epoch 94/100
218/218 [==============================] - 0s 961us/step - loss: 0.4644 - val_loss: 0.4711
Epoch 95/100
218/218 [==============================] - 0s 958us/step - loss: 0.4634 - val_loss: 0.4692
Epoch 96/100
218/218 [==============================] - 0s 984us/step - loss: 0.4636 - val_loss: 0.4748
Epoch 97/100
218/218 [==============================] - 0s 963us/step - loss: 0.4616 - val_loss: 0.4715
Epoch 98/100
218/218 [==============================] - 0s 964us/step - loss: 0.4633 - val_loss: 0.4738
Epoch 99/100
218/218 [==============================] - 0s 965us/step - loss: 0.4613 - val_loss: 0.4777
Epoch 100/100
218/218 [==============================] - 0s 930us/step - loss: 0.4633 - val_loss: 0.4750
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.44 seconds.
Calculating affinities...
Calculated affinities in 0.26 seconds.
Calculated graph and diffusion operator in 13.71 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.85 seconds.
Calculating KMeans...
Calculated KMeans in 18.19 seconds.
Calculated landmark operator in 20.51 seconds.
Calculating optimal t...
Automatically selected t = 26
Calculated optimal t in 2.89 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.55 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.10 seconds.
Calculated PHATE in 53.77 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_281 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_282 (Dense) (None, 32) 2080 dense_281[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_282[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_282[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_283 (Dense) (None, 32) 128
_________________________________________________________________
dense_284 (Dense) (None, 64) 2112
_________________________________________________________________
dense_285 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_281 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_282 (Dense) (None, 32) 2080 dense_281[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_282[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_282[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_930 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_930 ( [()] 0 tf_op_layer_Shape_930[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_93 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_930[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_93[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_279 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_93 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_279[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_931 (TensorFl [(2,)] 0 tf_op_layer_Add_93[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_933 (TensorFl [(2,)] 0 tf_op_layer_Add_93[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_932 (TensorFl [(2,)] 0 tf_op_layer_Add_93[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_934 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_936 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_935 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_937 (TensorFl [(2,)] 0 tf_op_layer_Add_93[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_939 (TensorFl [(2,)] 0 tf_op_layer_Add_93[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_938 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_931 ( [()] 0 tf_op_layer_Shape_931[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_933 ( [()] 0 tf_op_layer_Shape_933[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_932 ( [()] 0 tf_op_layer_Shape_932[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_934 ( [()] 0 tf_op_layer_Shape_934[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_936 ( [()] 0 tf_op_layer_Shape_936[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_935 ( [()] 0 tf_op_layer_Shape_935[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_937 ( [()] 0 tf_op_layer_Shape_937[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_939 ( [()] 0 tf_op_layer_Shape_939[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_938 ( [()] 0 tf_op_layer_Shape_938[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_558/shape ( [(3,)] 0 tf_op_layer_strided_slice_931[0][
tf_op_layer_strided_slice_933[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_559/shape ( [(3,)] 0 tf_op_layer_strided_slice_932[0][
tf_op_layer_strided_slice_933[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_560/shape ( [(3,)] 0 tf_op_layer_strided_slice_934[0][
tf_op_layer_strided_slice_936[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_561/shape ( [(3,)] 0 tf_op_layer_strided_slice_935[0][
tf_op_layer_strided_slice_936[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_562/shape ( [(3,)] 0 tf_op_layer_strided_slice_937[0][
tf_op_layer_strided_slice_939[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_563/shape ( [(3,)] 0 tf_op_layer_strided_slice_938[0][
tf_op_layer_strided_slice_939[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_558 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_93[0][0]
tf_op_layer_Reshape_558/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_558/multiples [(3,)] 0 tf_op_layer_strided_slice_932[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_559 (Tensor [(1, None, None)] 0 tf_op_layer_Add_93[0][0]
tf_op_layer_Reshape_559/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_559/multiples [(3,)] 0 tf_op_layer_strided_slice_931[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_560 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_560/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_560/multiples [(3,)] 0 tf_op_layer_strided_slice_935[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_561 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_561/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_561/multiples [(3,)] 0 tf_op_layer_strided_slice_934[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_562 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_93[0][0]
tf_op_layer_Reshape_562/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_562/multiples [(3,)] 0 tf_op_layer_strided_slice_938[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_563 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_563/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_563/multiples [(3,)] 0 tf_op_layer_strided_slice_937[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_558 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_558[0][0]
tf_op_layer_Tile_558/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_559 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_559[0][0]
tf_op_layer_Tile_559/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_560 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_560[0][0]
tf_op_layer_Tile_560/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_561 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_561[0][0]
tf_op_layer_Tile_561/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_562 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_562[0][0]
tf_op_layer_Tile_562/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_563 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_563[0][0]
tf_op_layer_Tile_563/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_465 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_558[0][0]
tf_op_layer_Tile_559[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_466 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_560[0][0]
tf_op_layer_Tile_561[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_467 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_562[0][0]
tf_op_layer_Tile_563[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_372 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_465[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_373 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_466[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_374 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_467[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_651 (TensorFlo [(None, None)] 0 tf_op_layer_Square_372[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_652 (TensorFlo [(None, None)] 0 tf_op_layer_Square_373[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_653 (TensorFlo [(None, None)] 0 tf_op_layer_Square_374[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_279 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_651[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_279 (TensorFlo [()] 0 tf_op_layer_strided_slice_933[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_280 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_652[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_280 (TensorFlo [()] 0 tf_op_layer_strided_slice_936[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_281 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_653[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_281 (TensorFlo [()] 0 tf_op_layer_strided_slice_939[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_279 (Tensor [(None, None)] 0 tf_op_layer_Neg_279[0][0]
tf_op_layer_Cast_279[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_280 (Tensor [(None, None)] 0 tf_op_layer_Neg_280[0][0]
tf_op_layer_Cast_280[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_281 (Tensor [(None, None)] 0 tf_op_layer_Neg_281[0][0]
tf_op_layer_Cast_281[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_279 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_279[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_280 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_280[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_281 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_281[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_654 (TensorFlo [()] 0 tf_op_layer_Exp_279[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_655 (TensorFlo [()] 0 tf_op_layer_Exp_280[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_656 (TensorFlo [()] 0 tf_op_layer_Exp_281[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_469 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_186 (TensorFl [()] 0 tf_op_layer_Mean_654[0][0]
tf_op_layer_Mean_655[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_280 (TensorFlow [()] 0 tf_op_layer_Mean_656[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_375 (TensorF [(None, 97)] 0 tf_op_layer_Sub_469[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_468 (TensorFlow [()] 0 tf_op_layer_AddV2_186[0][0]
tf_op_layer_Mul_280[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_93 (TensorFlowO [(None,)] 0 tf_op_layer_Square_375[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_281 (TensorFlow [()] 0 tf_op_layer_Sub_468[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_187 (TensorFl [(None,)] 0 tf_op_layer_Sum_93[0][0]
tf_op_layer_Mul_281[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_657 (TensorFlo [()] 0 tf_op_layer_AddV2_187[0][0]
__________________________________________________________________________________________________
add_loss_93 (AddLoss) () 0 tf_op_layer_Mean_657[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.0818WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0943s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9019 - val_loss: 0.5068
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4181 - val_loss: 0.3921
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3846 - val_loss: 0.3880
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3765 - val_loss: 0.3745
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3710 - val_loss: 0.3719
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3678 - val_loss: 0.3688
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3651 - val_loss: 0.3678
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3623 - val_loss: 0.3677
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3600 - val_loss: 0.3605
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3588 - val_loss: 0.3606
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3580 - val_loss: 0.3601
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3565 - val_loss: 0.3620
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3553 - val_loss: 0.3575
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3556 - val_loss: 0.3601
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3568
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3527 - val_loss: 0.3607
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3514 - val_loss: 0.3580
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3517 - val_loss: 0.3570
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3519 - val_loss: 0.3581
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3524 - val_loss: 0.3539
Epoch 21/100
218/218 [==============================] - 0s 986us/step - loss: 0.3503 - val_loss: 0.3587
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3497 - val_loss: 0.3528
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3506 - val_loss: 0.3577
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3498 - val_loss: 0.3544
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3510 - val_loss: 0.3513
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3508
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3492 - val_loss: 0.3538
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3482 - val_loss: 0.3508
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3521
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3484 - val_loss: 0.3529
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3504
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3483 - val_loss: 0.3507
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3500
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3487
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3526
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3505
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3502
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3478
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3498
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3469
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3504
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3494
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3476
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3490
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3493
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3479
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3506
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3485
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3489
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3459
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3485
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3486
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3457
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3448
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3467
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3465
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3462
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3471
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3442
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3498
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3489
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3465
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3484
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3476
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3484
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3465
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3452
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3482
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3460
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3448
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3463
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3453
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3468
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3459
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3448
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3395 - val_loss: 0.3450
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3454
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3445
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3447
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3465
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3446
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3429
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3441
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3447
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3439
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3426
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3436
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3401 - val_loss: 0.3424
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3445
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3432
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3435
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3443
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3437
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3379 - val_loss: 0.3472
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3389 - val_loss: 0.3456
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3422
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3382 - val_loss: 0.3464
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3429
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3419
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3385 - val_loss: 0.3431
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_286 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_940 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_940 ( [()] 0 tf_op_layer_Shape_940[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_94 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_940[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_94[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_282 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_94 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_282[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_941 (TensorFl [(2,)] 0 tf_op_layer_Add_94[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_943 (TensorFl [(2,)] 0 tf_op_layer_Add_94[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_942 (TensorFl [(2,)] 0 tf_op_layer_Add_94[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_944 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_946 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_945 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_947 (TensorFl [(2,)] 0 tf_op_layer_Add_94[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_949 (TensorFl [(2,)] 0 tf_op_layer_Add_94[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_948 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_941 ( [()] 0 tf_op_layer_Shape_941[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_943 ( [()] 0 tf_op_layer_Shape_943[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_942 ( [()] 0 tf_op_layer_Shape_942[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_944 ( [()] 0 tf_op_layer_Shape_944[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_946 ( [()] 0 tf_op_layer_Shape_946[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_945 ( [()] 0 tf_op_layer_Shape_945[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_947 ( [()] 0 tf_op_layer_Shape_947[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_949 ( [()] 0 tf_op_layer_Shape_949[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_948 ( [()] 0 tf_op_layer_Shape_948[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_564/shape ( [(3,)] 0 tf_op_layer_strided_slice_941[0][
tf_op_layer_strided_slice_943[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_565/shape ( [(3,)] 0 tf_op_layer_strided_slice_942[0][
tf_op_layer_strided_slice_943[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_566/shape ( [(3,)] 0 tf_op_layer_strided_slice_944[0][
tf_op_layer_strided_slice_946[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_567/shape ( [(3,)] 0 tf_op_layer_strided_slice_945[0][
tf_op_layer_strided_slice_946[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_568/shape ( [(3,)] 0 tf_op_layer_strided_slice_947[0][
tf_op_layer_strided_slice_949[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_569/shape ( [(3,)] 0 tf_op_layer_strided_slice_948[0][
tf_op_layer_strided_slice_949[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_564 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_94[0][0]
tf_op_layer_Reshape_564/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_564/multiples [(3,)] 0 tf_op_layer_strided_slice_942[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_565 (Tensor [(1, None, None)] 0 tf_op_layer_Add_94[0][0]
tf_op_layer_Reshape_565/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_565/multiples [(3,)] 0 tf_op_layer_strided_slice_941[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_566 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_566/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_566/multiples [(3,)] 0 tf_op_layer_strided_slice_945[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_567 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_567/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_567/multiples [(3,)] 0 tf_op_layer_strided_slice_944[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_568 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_94[0][0]
tf_op_layer_Reshape_568/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_568/multiples [(3,)] 0 tf_op_layer_strided_slice_948[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_569 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_569/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_569/multiples [(3,)] 0 tf_op_layer_strided_slice_947[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_564 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_564[0][0]
tf_op_layer_Tile_564/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_565 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_565[0][0]
tf_op_layer_Tile_565/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_566 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_566[0][0]
tf_op_layer_Tile_566/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_567 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_567[0][0]
tf_op_layer_Tile_567/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_568 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_568[0][0]
tf_op_layer_Tile_568/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_569 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_569[0][0]
tf_op_layer_Tile_569/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_470 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_564[0][0]
tf_op_layer_Tile_565[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_471 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_566[0][0]
tf_op_layer_Tile_567[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_472 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_568[0][0]
tf_op_layer_Tile_569[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_376 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_470[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_377 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_471[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_378 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_472[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_658 (TensorFlo [(None, None)] 0 tf_op_layer_Square_376[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_659 (TensorFlo [(None, None)] 0 tf_op_layer_Square_377[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_660 (TensorFlo [(None, None)] 0 tf_op_layer_Square_378[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_282 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_658[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_282 (TensorFlo [()] 0 tf_op_layer_strided_slice_943[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_283 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_659[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_283 (TensorFlo [()] 0 tf_op_layer_strided_slice_946[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_284 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_660[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_284 (TensorFlo [()] 0 tf_op_layer_strided_slice_949[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_282 (Tensor [(None, None)] 0 tf_op_layer_Neg_282[0][0]
tf_op_layer_Cast_282[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_283 (Tensor [(None, None)] 0 tf_op_layer_Neg_283[0][0]
tf_op_layer_Cast_283[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_284 (Tensor [(None, None)] 0 tf_op_layer_Neg_284[0][0]
tf_op_layer_Cast_284[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_282 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_282[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_283 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_283[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_284 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_284[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_661 (TensorFlo [()] 0 tf_op_layer_Exp_282[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_662 (TensorFlo [()] 0 tf_op_layer_Exp_283[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_663 (TensorFlo [()] 0 tf_op_layer_Exp_284[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_474 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_188 (TensorFl [()] 0 tf_op_layer_Mean_661[0][0]
tf_op_layer_Mean_662[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_283 (TensorFlow [()] 0 tf_op_layer_Mean_663[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_379 (TensorF [(None, 97)] 0 tf_op_layer_Sub_474[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_473 (TensorFlow [()] 0 tf_op_layer_AddV2_188[0][0]
tf_op_layer_Mul_283[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_94 (TensorFlowO [(None,)] 0 tf_op_layer_Square_379[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_284 (TensorFlow [()] 0 tf_op_layer_Sub_473[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_189 (TensorFl [(None,)] 0 tf_op_layer_Sum_94[0][0]
tf_op_layer_Mul_284[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_664 (TensorFlo [()] 0 tf_op_layer_AddV2_189[0][0]
__________________________________________________________________________________________________
add_loss_94 (AddLoss) () 0 tf_op_layer_Mean_664[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.8144WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0938s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 2.0491 - val_loss: 1.0238
Epoch 2/100
218/218 [==============================] - 0s 967us/step - loss: 0.8731 - val_loss: 0.8053
Epoch 3/100
218/218 [==============================] - 0s 977us/step - loss: 0.7078 - val_loss: 0.6886
Epoch 4/100
218/218 [==============================] - 0s 977us/step - loss: 0.6326 - val_loss: 0.6355
Epoch 5/100
218/218 [==============================] - 0s 991us/step - loss: 0.5966 - val_loss: 0.5970
Epoch 6/100
218/218 [==============================] - 0s 967us/step - loss: 0.5768 - val_loss: 0.5848
Epoch 7/100
218/218 [==============================] - 0s 962us/step - loss: 0.5604 - val_loss: 0.5746
Epoch 8/100
218/218 [==============================] - 0s 960us/step - loss: 0.5471 - val_loss: 0.5659
Epoch 9/100
218/218 [==============================] - 0s 967us/step - loss: 0.5393 - val_loss: 0.5558
Epoch 10/100
218/218 [==============================] - 0s 966us/step - loss: 0.5303 - val_loss: 0.5439
Epoch 11/100
218/218 [==============================] - 0s 963us/step - loss: 0.5250 - val_loss: 0.5401
Epoch 12/100
218/218 [==============================] - 0s 965us/step - loss: 0.5198 - val_loss: 0.5268
Epoch 13/100
218/218 [==============================] - 0s 963us/step - loss: 0.5189 - val_loss: 0.5322
Epoch 14/100
218/218 [==============================] - 0s 975us/step - loss: 0.5112 - val_loss: 0.5232
Epoch 15/100
218/218 [==============================] - 0s 980us/step - loss: 0.5075 - val_loss: 0.5270
Epoch 16/100
218/218 [==============================] - 0s 975us/step - loss: 0.5001 - val_loss: 0.5186
Epoch 17/100
218/218 [==============================] - 0s 982us/step - loss: 0.5012 - val_loss: 0.5191
Epoch 18/100
218/218 [==============================] - 0s 965us/step - loss: 0.4952 - val_loss: 0.5158
Epoch 19/100
218/218 [==============================] - 0s 967us/step - loss: 0.4952 - val_loss: 0.5119
Epoch 20/100
218/218 [==============================] - 0s 968us/step - loss: 0.4949 - val_loss: 0.5091
Epoch 21/100
218/218 [==============================] - 0s 962us/step - loss: 0.4909 - val_loss: 0.5073
Epoch 22/100
218/218 [==============================] - 0s 961us/step - loss: 0.4889 - val_loss: 0.5015
Epoch 23/100
218/218 [==============================] - 0s 968us/step - loss: 0.4884 - val_loss: 0.5019
Epoch 24/100
218/218 [==============================] - 0s 966us/step - loss: 0.4859 - val_loss: 0.4995
Epoch 25/100
218/218 [==============================] - 0s 965us/step - loss: 0.4827 - val_loss: 0.5054
Epoch 26/100
218/218 [==============================] - 0s 965us/step - loss: 0.4851 - val_loss: 0.5040
Epoch 27/100
218/218 [==============================] - 0s 968us/step - loss: 0.4827 - val_loss: 0.4986
Epoch 28/100
218/218 [==============================] - 0s 976us/step - loss: 0.4818 - val_loss: 0.5009
Epoch 29/100
218/218 [==============================] - 0s 970us/step - loss: 0.4816 - val_loss: 0.5024
Epoch 30/100
218/218 [==============================] - 0s 975us/step - loss: 0.4785 - val_loss: 0.4962
Epoch 31/100
218/218 [==============================] - 0s 969us/step - loss: 0.4777 - val_loss: 0.4974
Epoch 32/100
218/218 [==============================] - 0s 963us/step - loss: 0.4768 - val_loss: 0.4979
Epoch 33/100
218/218 [==============================] - 0s 959us/step - loss: 0.4769 - val_loss: 0.4971
Epoch 34/100
218/218 [==============================] - 0s 966us/step - loss: 0.4762 - val_loss: 0.4951
Epoch 35/100
218/218 [==============================] - 0s 971us/step - loss: 0.4762 - val_loss: 0.4887
Epoch 36/100
218/218 [==============================] - 0s 967us/step - loss: 0.4727 - val_loss: 0.4894
Epoch 37/100
218/218 [==============================] - 0s 962us/step - loss: 0.4720 - val_loss: 0.4913
Epoch 38/100
218/218 [==============================] - 0s 957us/step - loss: 0.4712 - val_loss: 0.4870
Epoch 39/100
218/218 [==============================] - 0s 958us/step - loss: 0.4709 - val_loss: 0.4823
Epoch 40/100
218/218 [==============================] - 0s 959us/step - loss: 0.4698 - val_loss: 0.4890
Epoch 41/100
218/218 [==============================] - 0s 968us/step - loss: 0.4708 - val_loss: 0.4830
Epoch 42/100
218/218 [==============================] - 0s 970us/step - loss: 0.4686 - val_loss: 0.4868
Epoch 43/100
218/218 [==============================] - 0s 963us/step - loss: 0.4703 - val_loss: 0.4863
Epoch 44/100
218/218 [==============================] - 0s 960us/step - loss: 0.4692 - val_loss: 0.4856
Epoch 45/100
218/218 [==============================] - 0s 957us/step - loss: 0.4693 - val_loss: 0.4823
Epoch 46/100
218/218 [==============================] - 0s 961us/step - loss: 0.4671 - val_loss: 0.4845
Epoch 47/100
218/218 [==============================] - 0s 959us/step - loss: 0.4674 - val_loss: 0.4884
Epoch 48/100
218/218 [==============================] - 0s 964us/step - loss: 0.4686 - val_loss: 0.4842
Epoch 49/100
218/218 [==============================] - 0s 965us/step - loss: 0.4665 - val_loss: 0.4855
Epoch 50/100
218/218 [==============================] - 0s 968us/step - loss: 0.4690 - val_loss: 0.4835
Epoch 51/100
218/218 [==============================] - 0s 972us/step - loss: 0.4656 - val_loss: 0.4831
Epoch 52/100
218/218 [==============================] - 0s 985us/step - loss: 0.4673 - val_loss: 0.4815
Epoch 53/100
218/218 [==============================] - 0s 967us/step - loss: 0.4635 - val_loss: 0.4831
Epoch 54/100
218/218 [==============================] - 0s 970us/step - loss: 0.4661 - val_loss: 0.4821
Epoch 55/100
218/218 [==============================] - 0s 969us/step - loss: 0.4636 - val_loss: 0.4826
Epoch 56/100
218/218 [==============================] - 0s 971us/step - loss: 0.4627 - val_loss: 0.4836
Epoch 57/100
218/218 [==============================] - 0s 962us/step - loss: 0.4649 - val_loss: 0.4789
Epoch 58/100
218/218 [==============================] - 0s 969us/step - loss: 0.4642 - val_loss: 0.4863
Epoch 59/100
218/218 [==============================] - 0s 965us/step - loss: 0.4632 - val_loss: 0.4794
Epoch 60/100
218/218 [==============================] - 0s 963us/step - loss: 0.4619 - val_loss: 0.4813
Epoch 61/100
218/218 [==============================] - 0s 965us/step - loss: 0.4623 - val_loss: 0.4833
Epoch 62/100
218/218 [==============================] - 0s 962us/step - loss: 0.4626 - val_loss: 0.4764
Epoch 63/100
218/218 [==============================] - 0s 965us/step - loss: 0.4632 - val_loss: 0.4795
Epoch 64/100
218/218 [==============================] - 0s 974us/step - loss: 0.4635 - val_loss: 0.4754
Epoch 65/100
218/218 [==============================] - 0s 987us/step - loss: 0.4633 - val_loss: 0.4781
Epoch 66/100
218/218 [==============================] - 0s 984us/step - loss: 0.4631 - val_loss: 0.4786
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4613 - val_loss: 0.4787
Epoch 68/100
218/218 [==============================] - 0s 996us/step - loss: 0.4596 - val_loss: 0.4787
Epoch 69/100
218/218 [==============================] - 0s 971us/step - loss: 0.4633 - val_loss: 0.4771
Epoch 70/100
218/218 [==============================] - 0s 982us/step - loss: 0.4592 - val_loss: 0.4804
Epoch 71/100
218/218 [==============================] - 0s 979us/step - loss: 0.4595 - val_loss: 0.4795
Epoch 72/100
218/218 [==============================] - 0s 983us/step - loss: 0.4611 - val_loss: 0.4759
Epoch 73/100
218/218 [==============================] - 0s 981us/step - loss: 0.4591 - val_loss: 0.4784
Epoch 74/100
218/218 [==============================] - 0s 975us/step - loss: 0.4609 - val_loss: 0.4778
Epoch 75/100
218/218 [==============================] - 0s 982us/step - loss: 0.4625 - val_loss: 0.4762
Epoch 76/100
218/218 [==============================] - 0s 982us/step - loss: 0.4585 - val_loss: 0.4773
Epoch 77/100
218/218 [==============================] - 0s 969us/step - loss: 0.4582 - val_loss: 0.4762
Epoch 78/100
218/218 [==============================] - 0s 971us/step - loss: 0.4588 - val_loss: 0.4723
Epoch 79/100
218/218 [==============================] - 0s 977us/step - loss: 0.4601 - val_loss: 0.4744
Epoch 80/100
218/218 [==============================] - 0s 967us/step - loss: 0.4597 - val_loss: 0.4782
Epoch 81/100
218/218 [==============================] - 0s 966us/step - loss: 0.4598 - val_loss: 0.4759
Epoch 82/100
218/218 [==============================] - 0s 969us/step - loss: 0.4579 - val_loss: 0.4713
Epoch 83/100
218/218 [==============================] - 0s 985us/step - loss: 0.4568 - val_loss: 0.4757
Epoch 84/100
218/218 [==============================] - 0s 987us/step - loss: 0.4584 - val_loss: 0.4720
Epoch 85/100
218/218 [==============================] - 0s 976us/step - loss: 0.4580 - val_loss: 0.4730
Epoch 86/100
218/218 [==============================] - 0s 966us/step - loss: 0.4568 - val_loss: 0.4755
Epoch 87/100
218/218 [==============================] - 0s 973us/step - loss: 0.4572 - val_loss: 0.4775
Epoch 88/100
218/218 [==============================] - 0s 979us/step - loss: 0.4558 - val_loss: 0.4752
Epoch 89/100
218/218 [==============================] - 0s 981us/step - loss: 0.4568 - val_loss: 0.4738
Epoch 90/100
218/218 [==============================] - 0s 974us/step - loss: 0.4584 - val_loss: 0.4765
Epoch 91/100
218/218 [==============================] - 0s 968us/step - loss: 0.4579 - val_loss: 0.4712
Epoch 92/100
218/218 [==============================] - 0s 972us/step - loss: 0.4571 - val_loss: 0.4745
Epoch 93/100
218/218 [==============================] - 0s 974us/step - loss: 0.4572 - val_loss: 0.4743
Epoch 94/100
218/218 [==============================] - 0s 975us/step - loss: 0.4555 - val_loss: 0.4749
Epoch 95/100
218/218 [==============================] - 0s 970us/step - loss: 0.4585 - val_loss: 0.4701
Epoch 96/100
218/218 [==============================] - 0s 977us/step - loss: 0.4580 - val_loss: 0.4739
Epoch 97/100
218/218 [==============================] - 0s 976us/step - loss: 0.4571 - val_loss: 0.4740
Epoch 98/100
218/218 [==============================] - 0s 988us/step - loss: 0.4553 - val_loss: 0.4724
Epoch 99/100
218/218 [==============================] - 0s 979us/step - loss: 0.4575 - val_loss: 0.4744
Epoch 100/100
218/218 [==============================] - 0s 981us/step - loss: 0.4552 - val_loss: 0.4693
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.46 seconds.
Calculating affinities...
Calculated affinities in 0.24 seconds.
Calculated graph and diffusion operator in 13.72 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.90 seconds.
Calculating KMeans...
Calculated KMeans in 23.45 seconds.
Calculated landmark operator in 25.81 seconds.
Calculating optimal t...
Automatically selected t = 29
Calculated optimal t in 2.85 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.62 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.57 seconds.
Calculated PHATE in 59.58 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_287 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_288 (Dense) (None, 32) 2080 dense_287[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_288[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_288[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_289 (Dense) (None, 32) 128
_________________________________________________________________
dense_290 (Dense) (None, 64) 2112
_________________________________________________________________
dense_291 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_287 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_288 (Dense) (None, 32) 2080 dense_287[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_288[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_288[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_950 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_950 ( [()] 0 tf_op_layer_Shape_950[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_95 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_950[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_95[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_285 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_95 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_285[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_951 (TensorFl [(2,)] 0 tf_op_layer_Add_95[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_953 (TensorFl [(2,)] 0 tf_op_layer_Add_95[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_952 (TensorFl [(2,)] 0 tf_op_layer_Add_95[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_954 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_956 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_955 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_957 (TensorFl [(2,)] 0 tf_op_layer_Add_95[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_959 (TensorFl [(2,)] 0 tf_op_layer_Add_95[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_958 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_951 ( [()] 0 tf_op_layer_Shape_951[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_953 ( [()] 0 tf_op_layer_Shape_953[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_952 ( [()] 0 tf_op_layer_Shape_952[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_954 ( [()] 0 tf_op_layer_Shape_954[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_956 ( [()] 0 tf_op_layer_Shape_956[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_955 ( [()] 0 tf_op_layer_Shape_955[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_957 ( [()] 0 tf_op_layer_Shape_957[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_959 ( [()] 0 tf_op_layer_Shape_959[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_958 ( [()] 0 tf_op_layer_Shape_958[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_570/shape ( [(3,)] 0 tf_op_layer_strided_slice_951[0][
tf_op_layer_strided_slice_953[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_571/shape ( [(3,)] 0 tf_op_layer_strided_slice_952[0][
tf_op_layer_strided_slice_953[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_572/shape ( [(3,)] 0 tf_op_layer_strided_slice_954[0][
tf_op_layer_strided_slice_956[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_573/shape ( [(3,)] 0 tf_op_layer_strided_slice_955[0][
tf_op_layer_strided_slice_956[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_574/shape ( [(3,)] 0 tf_op_layer_strided_slice_957[0][
tf_op_layer_strided_slice_959[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_575/shape ( [(3,)] 0 tf_op_layer_strided_slice_958[0][
tf_op_layer_strided_slice_959[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_570 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_95[0][0]
tf_op_layer_Reshape_570/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_570/multiples [(3,)] 0 tf_op_layer_strided_slice_952[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_571 (Tensor [(1, None, None)] 0 tf_op_layer_Add_95[0][0]
tf_op_layer_Reshape_571/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_571/multiples [(3,)] 0 tf_op_layer_strided_slice_951[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_572 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_572/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_572/multiples [(3,)] 0 tf_op_layer_strided_slice_955[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_573 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_573/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_573/multiples [(3,)] 0 tf_op_layer_strided_slice_954[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_574 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_95[0][0]
tf_op_layer_Reshape_574/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_574/multiples [(3,)] 0 tf_op_layer_strided_slice_958[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_575 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_575/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_575/multiples [(3,)] 0 tf_op_layer_strided_slice_957[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_570 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_570[0][0]
tf_op_layer_Tile_570/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_571 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_571[0][0]
tf_op_layer_Tile_571/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_572 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_572[0][0]
tf_op_layer_Tile_572/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_573 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_573[0][0]
tf_op_layer_Tile_573/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_574 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_574[0][0]
tf_op_layer_Tile_574/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_575 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_575[0][0]
tf_op_layer_Tile_575/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_475 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_570[0][0]
tf_op_layer_Tile_571[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_476 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_572[0][0]
tf_op_layer_Tile_573[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_477 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_574[0][0]
tf_op_layer_Tile_575[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_380 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_475[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_381 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_476[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_382 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_477[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_665 (TensorFlo [(None, None)] 0 tf_op_layer_Square_380[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_666 (TensorFlo [(None, None)] 0 tf_op_layer_Square_381[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_667 (TensorFlo [(None, None)] 0 tf_op_layer_Square_382[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_285 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_665[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_285 (TensorFlo [()] 0 tf_op_layer_strided_slice_953[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_286 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_666[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_286 (TensorFlo [()] 0 tf_op_layer_strided_slice_956[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_287 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_667[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_287 (TensorFlo [()] 0 tf_op_layer_strided_slice_959[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_285 (Tensor [(None, None)] 0 tf_op_layer_Neg_285[0][0]
tf_op_layer_Cast_285[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_286 (Tensor [(None, None)] 0 tf_op_layer_Neg_286[0][0]
tf_op_layer_Cast_286[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_287 (Tensor [(None, None)] 0 tf_op_layer_Neg_287[0][0]
tf_op_layer_Cast_287[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_285 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_285[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_286 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_286[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_287 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_287[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_668 (TensorFlo [()] 0 tf_op_layer_Exp_285[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_669 (TensorFlo [()] 0 tf_op_layer_Exp_286[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_670 (TensorFlo [()] 0 tf_op_layer_Exp_287[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_479 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_190 (TensorFl [()] 0 tf_op_layer_Mean_668[0][0]
tf_op_layer_Mean_669[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_286 (TensorFlow [()] 0 tf_op_layer_Mean_670[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_383 (TensorF [(None, 97)] 0 tf_op_layer_Sub_479[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_478 (TensorFlow [()] 0 tf_op_layer_AddV2_190[0][0]
tf_op_layer_Mul_286[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_95 (TensorFlowO [(None,)] 0 tf_op_layer_Square_383[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_287 (TensorFlow [()] 0 tf_op_layer_Sub_478[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_191 (TensorFl [(None,)] 0 tf_op_layer_Sum_95[0][0]
tf_op_layer_Mul_287[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_671 (TensorFlo [()] 0 tf_op_layer_AddV2_191[0][0]
__________________________________________________________________________________________________
add_loss_95 (AddLoss) () 0 tf_op_layer_Mean_671[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 14s - loss: 7.6810WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.1288s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 1.0008 - val_loss: 0.5205
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5080 - val_loss: 0.4819
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4422 - val_loss: 0.3979
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3936 - val_loss: 0.3795
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3846 - val_loss: 0.3745
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3777 - val_loss: 0.3723
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3750 - val_loss: 0.3687
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3716 - val_loss: 0.3681
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3687 - val_loss: 0.3656
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3672 - val_loss: 0.3648
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3647 - val_loss: 0.3623
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3637 - val_loss: 0.3599
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3621 - val_loss: 0.3609
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3612 - val_loss: 0.3587
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3588 - val_loss: 0.3545
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3575 - val_loss: 0.3566
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3571 - val_loss: 0.3531
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3572 - val_loss: 0.3547
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3538 - val_loss: 0.3541
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3528 - val_loss: 0.3508
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3534 - val_loss: 0.3536
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3540 - val_loss: 0.3505
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3546 - val_loss: 0.3555
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3528 - val_loss: 0.3535
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3517 - val_loss: 0.3527
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3522 - val_loss: 0.3485
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3514 - val_loss: 0.3484
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3516 - val_loss: 0.3505
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3506 - val_loss: 0.3484
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3487
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3508 - val_loss: 0.3498
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3508 - val_loss: 0.3463
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3497
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3482
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3454
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3491
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3454
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3461
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3474 - val_loss: 0.3485
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3490
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3455
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3451
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3464
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3457
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3464
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3454
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3451
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3441
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3476
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3440
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3460
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3451
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3449
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3436
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3500
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3447
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3454
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3452
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3443
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3420
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3452
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3425
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3445
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3425
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3426
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3419
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3413
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3442
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3430
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3449
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3398
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3412
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3441
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3438
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3427
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3452
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3426
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3418
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3445
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3414
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3415
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3435
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3454
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3415
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3437
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3401
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3436
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3417
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3411
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3429
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3430
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3405
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3413
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3427
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3417
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3403
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3415
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3389
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3423
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3411
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_292 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_960 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_960 ( [()] 0 tf_op_layer_Shape_960[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_96 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_960[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_96[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_288 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_96 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_288[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_961 (TensorFl [(2,)] 0 tf_op_layer_Add_96[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_963 (TensorFl [(2,)] 0 tf_op_layer_Add_96[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_962 (TensorFl [(2,)] 0 tf_op_layer_Add_96[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_964 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_966 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_965 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_967 (TensorFl [(2,)] 0 tf_op_layer_Add_96[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_969 (TensorFl [(2,)] 0 tf_op_layer_Add_96[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_968 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_961 ( [()] 0 tf_op_layer_Shape_961[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_963 ( [()] 0 tf_op_layer_Shape_963[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_962 ( [()] 0 tf_op_layer_Shape_962[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_964 ( [()] 0 tf_op_layer_Shape_964[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_966 ( [()] 0 tf_op_layer_Shape_966[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_965 ( [()] 0 tf_op_layer_Shape_965[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_967 ( [()] 0 tf_op_layer_Shape_967[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_969 ( [()] 0 tf_op_layer_Shape_969[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_968 ( [()] 0 tf_op_layer_Shape_968[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_576/shape ( [(3,)] 0 tf_op_layer_strided_slice_961[0][
tf_op_layer_strided_slice_963[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_577/shape ( [(3,)] 0 tf_op_layer_strided_slice_962[0][
tf_op_layer_strided_slice_963[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_578/shape ( [(3,)] 0 tf_op_layer_strided_slice_964[0][
tf_op_layer_strided_slice_966[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_579/shape ( [(3,)] 0 tf_op_layer_strided_slice_965[0][
tf_op_layer_strided_slice_966[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_580/shape ( [(3,)] 0 tf_op_layer_strided_slice_967[0][
tf_op_layer_strided_slice_969[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_581/shape ( [(3,)] 0 tf_op_layer_strided_slice_968[0][
tf_op_layer_strided_slice_969[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_576 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_96[0][0]
tf_op_layer_Reshape_576/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_576/multiples [(3,)] 0 tf_op_layer_strided_slice_962[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_577 (Tensor [(1, None, None)] 0 tf_op_layer_Add_96[0][0]
tf_op_layer_Reshape_577/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_577/multiples [(3,)] 0 tf_op_layer_strided_slice_961[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_578 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_578/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_578/multiples [(3,)] 0 tf_op_layer_strided_slice_965[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_579 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_579/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_579/multiples [(3,)] 0 tf_op_layer_strided_slice_964[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_580 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_96[0][0]
tf_op_layer_Reshape_580/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_580/multiples [(3,)] 0 tf_op_layer_strided_slice_968[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_581 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_581/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_581/multiples [(3,)] 0 tf_op_layer_strided_slice_967[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_576 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_576[0][0]
tf_op_layer_Tile_576/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_577 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_577[0][0]
tf_op_layer_Tile_577/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_578 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_578[0][0]
tf_op_layer_Tile_578/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_579 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_579[0][0]
tf_op_layer_Tile_579/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_580 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_580[0][0]
tf_op_layer_Tile_580/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_581 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_581[0][0]
tf_op_layer_Tile_581/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_480 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_576[0][0]
tf_op_layer_Tile_577[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_481 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_578[0][0]
tf_op_layer_Tile_579[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_482 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_580[0][0]
tf_op_layer_Tile_581[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_384 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_480[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_385 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_481[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_386 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_482[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_672 (TensorFlo [(None, None)] 0 tf_op_layer_Square_384[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_673 (TensorFlo [(None, None)] 0 tf_op_layer_Square_385[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_674 (TensorFlo [(None, None)] 0 tf_op_layer_Square_386[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_288 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_672[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_288 (TensorFlo [()] 0 tf_op_layer_strided_slice_963[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_289 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_673[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_289 (TensorFlo [()] 0 tf_op_layer_strided_slice_966[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_290 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_674[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_290 (TensorFlo [()] 0 tf_op_layer_strided_slice_969[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_288 (Tensor [(None, None)] 0 tf_op_layer_Neg_288[0][0]
tf_op_layer_Cast_288[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_289 (Tensor [(None, None)] 0 tf_op_layer_Neg_289[0][0]
tf_op_layer_Cast_289[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_290 (Tensor [(None, None)] 0 tf_op_layer_Neg_290[0][0]
tf_op_layer_Cast_290[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_288 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_288[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_289 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_289[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_290 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_290[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_675 (TensorFlo [()] 0 tf_op_layer_Exp_288[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_676 (TensorFlo [()] 0 tf_op_layer_Exp_289[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_677 (TensorFlo [()] 0 tf_op_layer_Exp_290[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_484 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_192 (TensorFl [()] 0 tf_op_layer_Mean_675[0][0]
tf_op_layer_Mean_676[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_289 (TensorFlow [()] 0 tf_op_layer_Mean_677[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_387 (TensorF [(None, 97)] 0 tf_op_layer_Sub_484[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_483 (TensorFlow [()] 0 tf_op_layer_AddV2_192[0][0]
tf_op_layer_Mul_289[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_96 (TensorFlowO [(None,)] 0 tf_op_layer_Square_387[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_290 (TensorFlow [()] 0 tf_op_layer_Sub_483[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_193 (TensorFl [(None,)] 0 tf_op_layer_Sum_96[0][0]
tf_op_layer_Mul_290[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_678 (TensorFlo [()] 0 tf_op_layer_AddV2_193[0][0]
__________________________________________________________________________________________________
add_loss_96 (AddLoss) () 0 tf_op_layer_Mean_678[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 7.9490WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.1068s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9391 - val_loss: 0.9332
Epoch 2/100
218/218 [==============================] - 0s 975us/step - loss: 0.8081 - val_loss: 0.7225
Epoch 3/100
218/218 [==============================] - 0s 948us/step - loss: 0.6781 - val_loss: 0.6440
Epoch 4/100
218/218 [==============================] - 0s 951us/step - loss: 0.6223 - val_loss: 0.6078
Epoch 5/100
218/218 [==============================] - 0s 949us/step - loss: 0.5939 - val_loss: 0.5928
Epoch 6/100
218/218 [==============================] - 0s 958us/step - loss: 0.5711 - val_loss: 0.5743
Epoch 7/100
218/218 [==============================] - 0s 954us/step - loss: 0.5640 - val_loss: 0.5546
Epoch 8/100
218/218 [==============================] - 0s 961us/step - loss: 0.5528 - val_loss: 0.5459
Epoch 9/100
218/218 [==============================] - 0s 954us/step - loss: 0.5433 - val_loss: 0.5433
Epoch 10/100
218/218 [==============================] - 0s 959us/step - loss: 0.5356 - val_loss: 0.5385
Epoch 11/100
218/218 [==============================] - 0s 958us/step - loss: 0.5293 - val_loss: 0.5262
Epoch 12/100
218/218 [==============================] - 0s 954us/step - loss: 0.5239 - val_loss: 0.5222
Epoch 13/100
218/218 [==============================] - 0s 950us/step - loss: 0.5157 - val_loss: 0.5132
Epoch 14/100
218/218 [==============================] - 0s 953us/step - loss: 0.5129 - val_loss: 0.5047
Epoch 15/100
218/218 [==============================] - 0s 950us/step - loss: 0.5110 - val_loss: 0.5138
Epoch 16/100
218/218 [==============================] - 0s 959us/step - loss: 0.5083 - val_loss: 0.5000
Epoch 17/100
218/218 [==============================] - 0s 950us/step - loss: 0.5036 - val_loss: 0.5062
Epoch 18/100
218/218 [==============================] - 0s 951us/step - loss: 0.5011 - val_loss: 0.4965
Epoch 19/100
218/218 [==============================] - 0s 956us/step - loss: 0.4984 - val_loss: 0.4941
Epoch 20/100
218/218 [==============================] - 0s 965us/step - loss: 0.4978 - val_loss: 0.4975
Epoch 21/100
218/218 [==============================] - 0s 958us/step - loss: 0.4935 - val_loss: 0.4978
Epoch 22/100
218/218 [==============================] - 0s 956us/step - loss: 0.4898 - val_loss: 0.4951
Epoch 23/100
218/218 [==============================] - 0s 953us/step - loss: 0.4932 - val_loss: 0.4939
Epoch 24/100
218/218 [==============================] - 0s 950us/step - loss: 0.4906 - val_loss: 0.4894
Epoch 25/100
218/218 [==============================] - 0s 951us/step - loss: 0.4879 - val_loss: 0.4868
Epoch 26/100
218/218 [==============================] - 0s 957us/step - loss: 0.4856 - val_loss: 0.4888
Epoch 27/100
218/218 [==============================] - 0s 947us/step - loss: 0.4841 - val_loss: 0.4837
Epoch 28/100
218/218 [==============================] - 0s 949us/step - loss: 0.4833 - val_loss: 0.4814
Epoch 29/100
218/218 [==============================] - 0s 946us/step - loss: 0.4820 - val_loss: 0.4817
Epoch 30/100
218/218 [==============================] - 0s 958us/step - loss: 0.4826 - val_loss: 0.4816
Epoch 31/100
218/218 [==============================] - 0s 955us/step - loss: 0.4795 - val_loss: 0.4873
Epoch 32/100
218/218 [==============================] - 0s 970us/step - loss: 0.4770 - val_loss: 0.4835
Epoch 33/100
218/218 [==============================] - 0s 954us/step - loss: 0.4777 - val_loss: 0.4821
Epoch 34/100
218/218 [==============================] - 0s 951us/step - loss: 0.4767 - val_loss: 0.4768
Epoch 35/100
218/218 [==============================] - 0s 956us/step - loss: 0.4771 - val_loss: 0.4747
Epoch 36/100
218/218 [==============================] - 0s 957us/step - loss: 0.4760 - val_loss: 0.4773
Epoch 37/100
218/218 [==============================] - 0s 964us/step - loss: 0.4763 - val_loss: 0.4730
Epoch 38/100
218/218 [==============================] - 0s 953us/step - loss: 0.4742 - val_loss: 0.4794
Epoch 39/100
218/218 [==============================] - 0s 950us/step - loss: 0.4735 - val_loss: 0.4713
Epoch 40/100
218/218 [==============================] - 0s 959us/step - loss: 0.4731 - val_loss: 0.4718
Epoch 41/100
218/218 [==============================] - 0s 951us/step - loss: 0.4739 - val_loss: 0.4714
Epoch 42/100
218/218 [==============================] - 0s 956us/step - loss: 0.4726 - val_loss: 0.4752
Epoch 43/100
218/218 [==============================] - 0s 950us/step - loss: 0.4704 - val_loss: 0.4705
Epoch 44/100
218/218 [==============================] - 0s 954us/step - loss: 0.4727 - val_loss: 0.4683
Epoch 45/100
218/218 [==============================] - 0s 958us/step - loss: 0.4733 - val_loss: 0.4734
Epoch 46/100
218/218 [==============================] - 0s 950us/step - loss: 0.4705 - val_loss: 0.4659
Epoch 47/100
218/218 [==============================] - 0s 951us/step - loss: 0.4696 - val_loss: 0.4692
Epoch 48/100
218/218 [==============================] - 0s 951us/step - loss: 0.4685 - val_loss: 0.4701
Epoch 49/100
218/218 [==============================] - 0s 959us/step - loss: 0.4694 - val_loss: 0.4757
Epoch 50/100
218/218 [==============================] - 0s 957us/step - loss: 0.4694 - val_loss: 0.4670
Epoch 51/100
218/218 [==============================] - 0s 968us/step - loss: 0.4681 - val_loss: 0.4708
Epoch 52/100
218/218 [==============================] - 0s 952us/step - loss: 0.4680 - val_loss: 0.4688
Epoch 53/100
218/218 [==============================] - 0s 949us/step - loss: 0.4668 - val_loss: 0.4667
Epoch 54/100
218/218 [==============================] - 0s 953us/step - loss: 0.4670 - val_loss: 0.4659
Epoch 55/100
218/218 [==============================] - 0s 958us/step - loss: 0.4667 - val_loss: 0.4710
Epoch 56/100
218/218 [==============================] - 0s 952us/step - loss: 0.4670 - val_loss: 0.4675
Epoch 57/100
218/218 [==============================] - 0s 954us/step - loss: 0.4651 - val_loss: 0.4664
Epoch 58/100
218/218 [==============================] - 0s 949us/step - loss: 0.4670 - val_loss: 0.4678
Epoch 59/100
218/218 [==============================] - 0s 958us/step - loss: 0.4650 - val_loss: 0.4674
Epoch 60/100
218/218 [==============================] - 0s 947us/step - loss: 0.4640 - val_loss: 0.4662
Epoch 61/100
218/218 [==============================] - 0s 955us/step - loss: 0.4656 - val_loss: 0.4646
Epoch 62/100
218/218 [==============================] - 0s 967us/step - loss: 0.4649 - val_loss: 0.4647
Epoch 63/100
218/218 [==============================] - 0s 958us/step - loss: 0.4632 - val_loss: 0.4611
Epoch 64/100
218/218 [==============================] - 0s 955us/step - loss: 0.4645 - val_loss: 0.4657
Epoch 65/100
218/218 [==============================] - 0s 956us/step - loss: 0.4645 - val_loss: 0.4646
Epoch 66/100
218/218 [==============================] - 0s 960us/step - loss: 0.4636 - val_loss: 0.4627
Epoch 67/100
218/218 [==============================] - 0s 964us/step - loss: 0.4638 - val_loss: 0.4615
Epoch 68/100
218/218 [==============================] - 0s 997us/step - loss: 0.4648 - val_loss: 0.4620
Epoch 69/100
218/218 [==============================] - 0s 970us/step - loss: 0.4616 - val_loss: 0.4604
Epoch 70/100
218/218 [==============================] - 0s 968us/step - loss: 0.4646 - val_loss: 0.4641
Epoch 71/100
218/218 [==============================] - 0s 972us/step - loss: 0.4625 - val_loss: 0.4558
Epoch 72/100
218/218 [==============================] - 0s 972us/step - loss: 0.4616 - val_loss: 0.4601
Epoch 73/100
218/218 [==============================] - 0s 975us/step - loss: 0.4631 - val_loss: 0.4609
Epoch 74/100
218/218 [==============================] - 0s 957us/step - loss: 0.4610 - val_loss: 0.4642
Epoch 75/100
218/218 [==============================] - 0s 958us/step - loss: 0.4620 - val_loss: 0.4665
Epoch 76/100
218/218 [==============================] - 0s 963us/step - loss: 0.4604 - val_loss: 0.4629
Epoch 77/100
218/218 [==============================] - 0s 971us/step - loss: 0.4612 - val_loss: 0.4658
Epoch 78/100
218/218 [==============================] - 0s 986us/step - loss: 0.4610 - val_loss: 0.4615
Epoch 79/100
218/218 [==============================] - 0s 972us/step - loss: 0.4602 - val_loss: 0.4637
Epoch 80/100
218/218 [==============================] - 0s 965us/step - loss: 0.4621 - val_loss: 0.4646
Epoch 81/100
218/218 [==============================] - 0s 973us/step - loss: 0.4626 - val_loss: 0.4633
Epoch 82/100
218/218 [==============================] - 0s 979us/step - loss: 0.4611 - val_loss: 0.4613
Epoch 83/100
218/218 [==============================] - 0s 964us/step - loss: 0.4619 - val_loss: 0.4599
Epoch 84/100
218/218 [==============================] - 0s 971us/step - loss: 0.4607 - val_loss: 0.4603
Epoch 85/100
218/218 [==============================] - 0s 954us/step - loss: 0.4602 - val_loss: 0.4636
Epoch 86/100
218/218 [==============================] - 0s 946us/step - loss: 0.4608 - val_loss: 0.4590
Epoch 87/100
218/218 [==============================] - 0s 963us/step - loss: 0.4592 - val_loss: 0.4598
Epoch 88/100
218/218 [==============================] - 0s 931us/step - loss: 0.4577 - val_loss: 0.4581
Epoch 89/100
218/218 [==============================] - 0s 965us/step - loss: 0.4607 - val_loss: 0.4612
Epoch 90/100
218/218 [==============================] - 0s 959us/step - loss: 0.4581 - val_loss: 0.4597
Epoch 91/100
218/218 [==============================] - 0s 937us/step - loss: 0.4591 - val_loss: 0.4604
Epoch 92/100
218/218 [==============================] - 0s 960us/step - loss: 0.4602 - val_loss: 0.4652
Epoch 93/100
218/218 [==============================] - 0s 961us/step - loss: 0.4600 - val_loss: 0.4619
Epoch 94/100
218/218 [==============================] - 0s 961us/step - loss: 0.4583 - val_loss: 0.4577
Epoch 95/100
218/218 [==============================] - 0s 960us/step - loss: 0.4591 - val_loss: 0.4607
Epoch 96/100
218/218 [==============================] - 0s 956us/step - loss: 0.4576 - val_loss: 0.4566
Epoch 97/100
218/218 [==============================] - 0s 962us/step - loss: 0.4592 - val_loss: 0.4596
Epoch 98/100
218/218 [==============================] - 0s 963us/step - loss: 0.4584 - val_loss: 0.4606
Epoch 99/100
218/218 [==============================] - 0s 961us/step - loss: 0.4581 - val_loss: 0.4572
Epoch 100/100
218/218 [==============================] - 0s 959us/step - loss: 0.4591 - val_loss: 0.4599
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.44 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.70 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.85 seconds.
Calculating KMeans...
Calculated KMeans in 15.96 seconds.
Calculated landmark operator in 18.26 seconds.
Calculating optimal t...
Automatically selected t = 48
Calculated optimal t in 2.88 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.54 seconds.
Calculating metric MDS...
Calculated metric MDS in 18.09 seconds.
Calculated PHATE in 53.49 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_293 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_294 (Dense) (None, 32) 2080 dense_293[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_294[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_294[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_295 (Dense) (None, 32) 128
_________________________________________________________________
dense_296 (Dense) (None, 64) 2112
_________________________________________________________________
dense_297 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_293 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_294 (Dense) (None, 32) 2080 dense_293[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_294[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_294[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_970 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_970 ( [()] 0 tf_op_layer_Shape_970[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_97 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_970[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_97[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_291 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_97 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_291[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_971 (TensorFl [(2,)] 0 tf_op_layer_Add_97[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_973 (TensorFl [(2,)] 0 tf_op_layer_Add_97[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_972 (TensorFl [(2,)] 0 tf_op_layer_Add_97[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_974 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_976 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_975 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_977 (TensorFl [(2,)] 0 tf_op_layer_Add_97[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_979 (TensorFl [(2,)] 0 tf_op_layer_Add_97[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_978 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_971 ( [()] 0 tf_op_layer_Shape_971[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_973 ( [()] 0 tf_op_layer_Shape_973[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_972 ( [()] 0 tf_op_layer_Shape_972[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_974 ( [()] 0 tf_op_layer_Shape_974[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_976 ( [()] 0 tf_op_layer_Shape_976[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_975 ( [()] 0 tf_op_layer_Shape_975[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_977 ( [()] 0 tf_op_layer_Shape_977[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_979 ( [()] 0 tf_op_layer_Shape_979[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_978 ( [()] 0 tf_op_layer_Shape_978[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_582/shape ( [(3,)] 0 tf_op_layer_strided_slice_971[0][
tf_op_layer_strided_slice_973[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_583/shape ( [(3,)] 0 tf_op_layer_strided_slice_972[0][
tf_op_layer_strided_slice_973[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_584/shape ( [(3,)] 0 tf_op_layer_strided_slice_974[0][
tf_op_layer_strided_slice_976[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_585/shape ( [(3,)] 0 tf_op_layer_strided_slice_975[0][
tf_op_layer_strided_slice_976[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_586/shape ( [(3,)] 0 tf_op_layer_strided_slice_977[0][
tf_op_layer_strided_slice_979[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_587/shape ( [(3,)] 0 tf_op_layer_strided_slice_978[0][
tf_op_layer_strided_slice_979[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_582 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_97[0][0]
tf_op_layer_Reshape_582/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_582/multiples [(3,)] 0 tf_op_layer_strided_slice_972[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_583 (Tensor [(1, None, None)] 0 tf_op_layer_Add_97[0][0]
tf_op_layer_Reshape_583/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_583/multiples [(3,)] 0 tf_op_layer_strided_slice_971[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_584 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_584/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_584/multiples [(3,)] 0 tf_op_layer_strided_slice_975[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_585 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_585/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_585/multiples [(3,)] 0 tf_op_layer_strided_slice_974[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_586 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_97[0][0]
tf_op_layer_Reshape_586/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_586/multiples [(3,)] 0 tf_op_layer_strided_slice_978[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_587 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_587/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_587/multiples [(3,)] 0 tf_op_layer_strided_slice_977[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_582 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_582[0][0]
tf_op_layer_Tile_582/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_583 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_583[0][0]
tf_op_layer_Tile_583/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_584 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_584[0][0]
tf_op_layer_Tile_584/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_585 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_585[0][0]
tf_op_layer_Tile_585/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_586 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_586[0][0]
tf_op_layer_Tile_586/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_587 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_587[0][0]
tf_op_layer_Tile_587/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_485 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_582[0][0]
tf_op_layer_Tile_583[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_486 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_584[0][0]
tf_op_layer_Tile_585[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_487 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_586[0][0]
tf_op_layer_Tile_587[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_388 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_485[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_389 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_486[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_390 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_487[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_679 (TensorFlo [(None, None)] 0 tf_op_layer_Square_388[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_680 (TensorFlo [(None, None)] 0 tf_op_layer_Square_389[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_681 (TensorFlo [(None, None)] 0 tf_op_layer_Square_390[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_291 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_679[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_291 (TensorFlo [()] 0 tf_op_layer_strided_slice_973[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_292 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_680[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_292 (TensorFlo [()] 0 tf_op_layer_strided_slice_976[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_293 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_681[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_293 (TensorFlo [()] 0 tf_op_layer_strided_slice_979[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_291 (Tensor [(None, None)] 0 tf_op_layer_Neg_291[0][0]
tf_op_layer_Cast_291[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_292 (Tensor [(None, None)] 0 tf_op_layer_Neg_292[0][0]
tf_op_layer_Cast_292[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_293 (Tensor [(None, None)] 0 tf_op_layer_Neg_293[0][0]
tf_op_layer_Cast_293[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_291 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_291[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_292 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_292[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_293 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_293[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_682 (TensorFlo [()] 0 tf_op_layer_Exp_291[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_683 (TensorFlo [()] 0 tf_op_layer_Exp_292[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_684 (TensorFlo [()] 0 tf_op_layer_Exp_293[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_489 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_194 (TensorFl [()] 0 tf_op_layer_Mean_682[0][0]
tf_op_layer_Mean_683[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_292 (TensorFlow [()] 0 tf_op_layer_Mean_684[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_391 (TensorF [(None, 97)] 0 tf_op_layer_Sub_489[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_488 (TensorFlow [()] 0 tf_op_layer_AddV2_194[0][0]
tf_op_layer_Mul_292[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_97 (TensorFlowO [(None,)] 0 tf_op_layer_Square_391[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_293 (TensorFlow [()] 0 tf_op_layer_Sub_488[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_195 (TensorFl [(None,)] 0 tf_op_layer_Sum_97[0][0]
tf_op_layer_Mul_293[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_685 (TensorFlo [()] 0 tf_op_layer_AddV2_195[0][0]
__________________________________________________________________________________________________
add_loss_97 (AddLoss) () 0 tf_op_layer_Mean_685[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 7.6294WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.1027s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9127 - val_loss: 0.4689
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4160 - val_loss: 0.3782
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3909 - val_loss: 0.3661
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3829 - val_loss: 0.3620
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3797 - val_loss: 0.3613
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3761 - val_loss: 0.3551
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3729 - val_loss: 0.3534
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3705 - val_loss: 0.3478
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3683 - val_loss: 0.3538
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3669 - val_loss: 0.3474
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3653 - val_loss: 0.3445
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3631 - val_loss: 0.3463
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3619 - val_loss: 0.3449
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3612 - val_loss: 0.3415
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3596 - val_loss: 0.3418
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3596 - val_loss: 0.3407
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3578 - val_loss: 0.3398
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3573 - val_loss: 0.3427
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3584 - val_loss: 0.3415
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3568 - val_loss: 0.3385
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3560 - val_loss: 0.3377
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3561 - val_loss: 0.3377
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3534 - val_loss: 0.3396
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3537 - val_loss: 0.3357
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3534 - val_loss: 0.3390
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3540 - val_loss: 0.3380
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3541 - val_loss: 0.3379
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3531 - val_loss: 0.3373
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3520 - val_loss: 0.3351
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3527 - val_loss: 0.3339
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3380
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3525 - val_loss: 0.3328
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3512 - val_loss: 0.3365
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3507 - val_loss: 0.3335
Epoch 35/100
218/218 [==============================] - 0s 995us/step - loss: 0.3516 - val_loss: 0.3359
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3509 - val_loss: 0.3344
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3352
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3507 - val_loss: 0.3324
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3498 - val_loss: 0.3354
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3296
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3496 - val_loss: 0.3324
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3312
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3317
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3497 - val_loss: 0.3328
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3298
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3304
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3293
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3302
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3327
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3298
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3334
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3347
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3324
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3324
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3483 - val_loss: 0.3323
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3290
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3327
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3298
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3474 - val_loss: 0.3309
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3314
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3303
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3284
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3259
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3482 - val_loss: 0.3280
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3296
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3300
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3303
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3298
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3302
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3290
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3293
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3308
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3292
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3271
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3338
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3297
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3287
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3297
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3272
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3309
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3270
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3293
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3288
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3271
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3287
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3279
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3271
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3295
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3283
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3248
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3309
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3275
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3270
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3261
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3298
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3299
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3299
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3278
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3266
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3265
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_298 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_980 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_980 ( [()] 0 tf_op_layer_Shape_980[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_98 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_980[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_98[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_294 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_98 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_294[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_981 (TensorFl [(2,)] 0 tf_op_layer_Add_98[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_983 (TensorFl [(2,)] 0 tf_op_layer_Add_98[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_982 (TensorFl [(2,)] 0 tf_op_layer_Add_98[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_984 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_986 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_985 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_987 (TensorFl [(2,)] 0 tf_op_layer_Add_98[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_989 (TensorFl [(2,)] 0 tf_op_layer_Add_98[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_988 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_981 ( [()] 0 tf_op_layer_Shape_981[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_983 ( [()] 0 tf_op_layer_Shape_983[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_982 ( [()] 0 tf_op_layer_Shape_982[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_984 ( [()] 0 tf_op_layer_Shape_984[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_986 ( [()] 0 tf_op_layer_Shape_986[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_985 ( [()] 0 tf_op_layer_Shape_985[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_987 ( [()] 0 tf_op_layer_Shape_987[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_989 ( [()] 0 tf_op_layer_Shape_989[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_988 ( [()] 0 tf_op_layer_Shape_988[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_588/shape ( [(3,)] 0 tf_op_layer_strided_slice_981[0][
tf_op_layer_strided_slice_983[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_589/shape ( [(3,)] 0 tf_op_layer_strided_slice_982[0][
tf_op_layer_strided_slice_983[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_590/shape ( [(3,)] 0 tf_op_layer_strided_slice_984[0][
tf_op_layer_strided_slice_986[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_591/shape ( [(3,)] 0 tf_op_layer_strided_slice_985[0][
tf_op_layer_strided_slice_986[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_592/shape ( [(3,)] 0 tf_op_layer_strided_slice_987[0][
tf_op_layer_strided_slice_989[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_593/shape ( [(3,)] 0 tf_op_layer_strided_slice_988[0][
tf_op_layer_strided_slice_989[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_588 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_98[0][0]
tf_op_layer_Reshape_588/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_588/multiples [(3,)] 0 tf_op_layer_strided_slice_982[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_589 (Tensor [(1, None, None)] 0 tf_op_layer_Add_98[0][0]
tf_op_layer_Reshape_589/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_589/multiples [(3,)] 0 tf_op_layer_strided_slice_981[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_590 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_590/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_590/multiples [(3,)] 0 tf_op_layer_strided_slice_985[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_591 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_591/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_591/multiples [(3,)] 0 tf_op_layer_strided_slice_984[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_592 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_98[0][0]
tf_op_layer_Reshape_592/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_592/multiples [(3,)] 0 tf_op_layer_strided_slice_988[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_593 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_593/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_593/multiples [(3,)] 0 tf_op_layer_strided_slice_987[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_588 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_588[0][0]
tf_op_layer_Tile_588/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_589 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_589[0][0]
tf_op_layer_Tile_589/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_590 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_590[0][0]
tf_op_layer_Tile_590/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_591 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_591[0][0]
tf_op_layer_Tile_591/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_592 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_592[0][0]
tf_op_layer_Tile_592/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_593 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_593[0][0]
tf_op_layer_Tile_593/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_490 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_588[0][0]
tf_op_layer_Tile_589[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_491 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_590[0][0]
tf_op_layer_Tile_591[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_492 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_592[0][0]
tf_op_layer_Tile_593[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_392 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_490[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_393 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_491[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_394 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_492[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_686 (TensorFlo [(None, None)] 0 tf_op_layer_Square_392[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_687 (TensorFlo [(None, None)] 0 tf_op_layer_Square_393[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_688 (TensorFlo [(None, None)] 0 tf_op_layer_Square_394[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_294 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_686[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_294 (TensorFlo [()] 0 tf_op_layer_strided_slice_983[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_295 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_687[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_295 (TensorFlo [()] 0 tf_op_layer_strided_slice_986[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_296 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_688[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_296 (TensorFlo [()] 0 tf_op_layer_strided_slice_989[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_294 (Tensor [(None, None)] 0 tf_op_layer_Neg_294[0][0]
tf_op_layer_Cast_294[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_295 (Tensor [(None, None)] 0 tf_op_layer_Neg_295[0][0]
tf_op_layer_Cast_295[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_296 (Tensor [(None, None)] 0 tf_op_layer_Neg_296[0][0]
tf_op_layer_Cast_296[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_294 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_294[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_295 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_295[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_296 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_296[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_689 (TensorFlo [()] 0 tf_op_layer_Exp_294[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_690 (TensorFlo [()] 0 tf_op_layer_Exp_295[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_691 (TensorFlo [()] 0 tf_op_layer_Exp_296[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_494 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_196 (TensorFl [()] 0 tf_op_layer_Mean_689[0][0]
tf_op_layer_Mean_690[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_295 (TensorFlow [()] 0 tf_op_layer_Mean_691[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_395 (TensorF [(None, 97)] 0 tf_op_layer_Sub_494[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_493 (TensorFlow [()] 0 tf_op_layer_AddV2_196[0][0]
tf_op_layer_Mul_295[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_98 (TensorFlowO [(None,)] 0 tf_op_layer_Square_395[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_296 (TensorFlow [()] 0 tf_op_layer_Sub_493[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_197 (TensorFl [(None,)] 0 tf_op_layer_Sum_98[0][0]
tf_op_layer_Mul_296[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_692 (TensorFlo [()] 0 tf_op_layer_AddV2_197[0][0]
__________________________________________________________________________________________________
add_loss_98 (AddLoss) () 0 tf_op_layer_Mean_692[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 8.1577WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0968s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9786 - val_loss: 0.9780
Epoch 2/100
218/218 [==============================] - 0s 938us/step - loss: 0.8790 - val_loss: 0.7936
Epoch 3/100
218/218 [==============================] - 0s 934us/step - loss: 0.7357 - val_loss: 0.6780
Epoch 4/100
218/218 [==============================] - 0s 940us/step - loss: 0.6563 - val_loss: 0.6299
Epoch 5/100
218/218 [==============================] - 0s 905us/step - loss: 0.6171 - val_loss: 0.5942
Epoch 6/100
218/218 [==============================] - 0s 931us/step - loss: 0.5967 - val_loss: 0.5718
Epoch 7/100
218/218 [==============================] - 0s 933us/step - loss: 0.5787 - val_loss: 0.5553
Epoch 8/100
218/218 [==============================] - 0s 940us/step - loss: 0.5666 - val_loss: 0.5503
Epoch 9/100
218/218 [==============================] - 0s 947us/step - loss: 0.5527 - val_loss: 0.5373
Epoch 10/100
218/218 [==============================] - 0s 981us/step - loss: 0.5475 - val_loss: 0.5298
Epoch 11/100
218/218 [==============================] - 0s 968us/step - loss: 0.5381 - val_loss: 0.5229
Epoch 12/100
218/218 [==============================] - 0s 946us/step - loss: 0.5316 - val_loss: 0.5247
Epoch 13/100
218/218 [==============================] - 0s 948us/step - loss: 0.5287 - val_loss: 0.5126
Epoch 14/100
218/218 [==============================] - 0s 955us/step - loss: 0.5244 - val_loss: 0.5076
Epoch 15/100
218/218 [==============================] - 0s 951us/step - loss: 0.5199 - val_loss: 0.5078
Epoch 16/100
218/218 [==============================] - 0s 940us/step - loss: 0.5128 - val_loss: 0.4997
Epoch 17/100
218/218 [==============================] - 0s 944us/step - loss: 0.5091 - val_loss: 0.4988
Epoch 18/100
218/218 [==============================] - 0s 952us/step - loss: 0.5085 - val_loss: 0.4937
Epoch 19/100
218/218 [==============================] - 0s 952us/step - loss: 0.5052 - val_loss: 0.4898
Epoch 20/100
218/218 [==============================] - 0s 939us/step - loss: 0.5039 - val_loss: 0.4897
Epoch 21/100
218/218 [==============================] - 0s 940us/step - loss: 0.5001 - val_loss: 0.4853
Epoch 22/100
218/218 [==============================] - 0s 942us/step - loss: 0.4980 - val_loss: 0.4863
Epoch 23/100
218/218 [==============================] - 0s 951us/step - loss: 0.4957 - val_loss: 0.4845
Epoch 24/100
218/218 [==============================] - 0s 956us/step - loss: 0.4928 - val_loss: 0.4850
Epoch 25/100
218/218 [==============================] - 0s 962us/step - loss: 0.4920 - val_loss: 0.4795
Epoch 26/100
218/218 [==============================] - 0s 932us/step - loss: 0.4892 - val_loss: 0.4789
Epoch 27/100
218/218 [==============================] - 0s 931us/step - loss: 0.4882 - val_loss: 0.4809
Epoch 28/100
218/218 [==============================] - 0s 932us/step - loss: 0.4896 - val_loss: 0.4770
Epoch 29/100
218/218 [==============================] - 0s 939us/step - loss: 0.4858 - val_loss: 0.4728
Epoch 30/100
218/218 [==============================] - 0s 951us/step - loss: 0.4866 - val_loss: 0.4681
Epoch 31/100
218/218 [==============================] - 0s 941us/step - loss: 0.4830 - val_loss: 0.4704
Epoch 32/100
218/218 [==============================] - 0s 937us/step - loss: 0.4842 - val_loss: 0.4713
Epoch 33/100
218/218 [==============================] - 0s 937us/step - loss: 0.4824 - val_loss: 0.4700
Epoch 34/100
218/218 [==============================] - 0s 933us/step - loss: 0.4842 - val_loss: 0.4665
Epoch 35/100
218/218 [==============================] - 0s 940us/step - loss: 0.4807 - val_loss: 0.4695
Epoch 36/100
218/218 [==============================] - 0s 941us/step - loss: 0.4798 - val_loss: 0.4694
Epoch 37/100
218/218 [==============================] - 0s 952us/step - loss: 0.4809 - val_loss: 0.4694
Epoch 38/100
218/218 [==============================] - 0s 892us/step - loss: 0.4812 - val_loss: 0.4701
Epoch 39/100
218/218 [==============================] - 0s 944us/step - loss: 0.4775 - val_loss: 0.4651
Epoch 40/100
218/218 [==============================] - 0s 958us/step - loss: 0.4770 - val_loss: 0.4637
Epoch 41/100
218/218 [==============================] - 0s 948us/step - loss: 0.4772 - val_loss: 0.4659
Epoch 42/100
218/218 [==============================] - 0s 937us/step - loss: 0.4808 - val_loss: 0.4620
Epoch 43/100
218/218 [==============================] - 0s 890us/step - loss: 0.4768 - val_loss: 0.4627
Epoch 44/100
218/218 [==============================] - 0s 939us/step - loss: 0.4746 - val_loss: 0.4630
Epoch 45/100
218/218 [==============================] - 0s 944us/step - loss: 0.4755 - val_loss: 0.4612
Epoch 46/100
218/218 [==============================] - 0s 944us/step - loss: 0.4739 - val_loss: 0.4621
Epoch 47/100
218/218 [==============================] - 0s 933us/step - loss: 0.4717 - val_loss: 0.4607
Epoch 48/100
218/218 [==============================] - 0s 933us/step - loss: 0.4735 - val_loss: 0.4571
Epoch 49/100
218/218 [==============================] - 0s 941us/step - loss: 0.4730 - val_loss: 0.4614
Epoch 50/100
218/218 [==============================] - 0s 938us/step - loss: 0.4726 - val_loss: 0.4586
Epoch 51/100
218/218 [==============================] - 0s 929us/step - loss: 0.4727 - val_loss: 0.4559
Epoch 52/100
218/218 [==============================] - 0s 926us/step - loss: 0.4693 - val_loss: 0.4584
Epoch 53/100
218/218 [==============================] - 0s 936us/step - loss: 0.4726 - val_loss: 0.4583
Epoch 54/100
218/218 [==============================] - 0s 931us/step - loss: 0.4710 - val_loss: 0.4594
Epoch 55/100
218/218 [==============================] - 0s 926us/step - loss: 0.4707 - val_loss: 0.4584
Epoch 56/100
218/218 [==============================] - 0s 924us/step - loss: 0.4703 - val_loss: 0.4570
Epoch 57/100
218/218 [==============================] - 0s 929us/step - loss: 0.4690 - val_loss: 0.4598
Epoch 58/100
218/218 [==============================] - 0s 938us/step - loss: 0.4711 - val_loss: 0.4564
Epoch 59/100
218/218 [==============================] - 0s 952us/step - loss: 0.4712 - val_loss: 0.4561
Epoch 60/100
218/218 [==============================] - 0s 917us/step - loss: 0.4680 - val_loss: 0.4587
Epoch 61/100
218/218 [==============================] - 0s 928us/step - loss: 0.4692 - val_loss: 0.4492
Epoch 62/100
218/218 [==============================] - 0s 938us/step - loss: 0.4675 - val_loss: 0.4581
Epoch 63/100
218/218 [==============================] - 0s 935us/step - loss: 0.4689 - val_loss: 0.4522
Epoch 64/100
218/218 [==============================] - 0s 934us/step - loss: 0.4679 - val_loss: 0.4541
Epoch 65/100
218/218 [==============================] - 0s 929us/step - loss: 0.4669 - val_loss: 0.4547
Epoch 66/100
218/218 [==============================] - 0s 927us/step - loss: 0.4670 - val_loss: 0.4563
Epoch 67/100
218/218 [==============================] - 0s 930us/step - loss: 0.4671 - val_loss: 0.4528
Epoch 68/100
218/218 [==============================] - 0s 942us/step - loss: 0.4668 - val_loss: 0.4519
Epoch 69/100
218/218 [==============================] - 0s 959us/step - loss: 0.4669 - val_loss: 0.4488
Epoch 70/100
218/218 [==============================] - 0s 954us/step - loss: 0.4654 - val_loss: 0.4524
Epoch 71/100
218/218 [==============================] - 0s 959us/step - loss: 0.4660 - val_loss: 0.4507
Epoch 72/100
218/218 [==============================] - 0s 964us/step - loss: 0.4649 - val_loss: 0.4525
Epoch 73/100
218/218 [==============================] - 0s 960us/step - loss: 0.4656 - val_loss: 0.4540
Epoch 74/100
218/218 [==============================] - 0s 966us/step - loss: 0.4678 - val_loss: 0.4459
Epoch 75/100
218/218 [==============================] - 0s 960us/step - loss: 0.4644 - val_loss: 0.4538
Epoch 76/100
218/218 [==============================] - 0s 960us/step - loss: 0.4656 - val_loss: 0.4507
Epoch 77/100
218/218 [==============================] - 0s 954us/step - loss: 0.4633 - val_loss: 0.4525
Epoch 78/100
218/218 [==============================] - 0s 961us/step - loss: 0.4629 - val_loss: 0.4512
Epoch 79/100
218/218 [==============================] - 0s 959us/step - loss: 0.4654 - val_loss: 0.4502
Epoch 80/100
218/218 [==============================] - 0s 961us/step - loss: 0.4653 - val_loss: 0.4513
Epoch 81/100
218/218 [==============================] - 0s 967us/step - loss: 0.4646 - val_loss: 0.4494
Epoch 82/100
218/218 [==============================] - 0s 982us/step - loss: 0.4636 - val_loss: 0.4496
Epoch 83/100
218/218 [==============================] - 0s 970us/step - loss: 0.4639 - val_loss: 0.4516
Epoch 84/100
218/218 [==============================] - 0s 957us/step - loss: 0.4631 - val_loss: 0.4506
Epoch 85/100
218/218 [==============================] - 0s 959us/step - loss: 0.4631 - val_loss: 0.4521
Epoch 86/100
218/218 [==============================] - 0s 959us/step - loss: 0.4619 - val_loss: 0.4503
Epoch 87/100
218/218 [==============================] - 0s 959us/step - loss: 0.4637 - val_loss: 0.4505
Epoch 88/100
218/218 [==============================] - 0s 964us/step - loss: 0.4631 - val_loss: 0.4495
Epoch 89/100
218/218 [==============================] - 0s 956us/step - loss: 0.4652 - val_loss: 0.4486
Epoch 90/100
218/218 [==============================] - 0s 957us/step - loss: 0.4617 - val_loss: 0.4533
Epoch 91/100
218/218 [==============================] - 0s 960us/step - loss: 0.4625 - val_loss: 0.4513
Epoch 92/100
218/218 [==============================] - 0s 964us/step - loss: 0.4631 - val_loss: 0.4448
Epoch 93/100
218/218 [==============================] - 0s 964us/step - loss: 0.4618 - val_loss: 0.4479
Epoch 94/100
218/218 [==============================] - 0s 960us/step - loss: 0.4620 - val_loss: 0.4455
Epoch 95/100
218/218 [==============================] - 0s 961us/step - loss: 0.4613 - val_loss: 0.4482
Epoch 96/100
218/218 [==============================] - 0s 966us/step - loss: 0.4599 - val_loss: 0.4474
Epoch 97/100
218/218 [==============================] - 0s 983us/step - loss: 0.4613 - val_loss: 0.4456
Epoch 98/100
218/218 [==============================] - 0s 969us/step - loss: 0.4623 - val_loss: 0.4479
Epoch 99/100
218/218 [==============================] - 0s 959us/step - loss: 0.4620 - val_loss: 0.4465
Epoch 100/100
218/218 [==============================] - 0s 957us/step - loss: 0.4621 - val_loss: 0.4516
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.39 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.66 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.86 seconds.
Calculating KMeans...
Calculated KMeans in 17.90 seconds.
Calculated landmark operator in 20.22 seconds.
Calculating optimal t...
Automatically selected t = 42
Calculated optimal t in 2.86 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.63 seconds.
Calculating metric MDS...
Calculated metric MDS in 17.93 seconds.
Calculated PHATE in 55.31 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_299 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_300 (Dense) (None, 32) 2080 dense_299[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_300[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_300[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_301 (Dense) (None, 32) 128
_________________________________________________________________
dense_302 (Dense) (None, 64) 2112
_________________________________________________________________
dense_303 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_299 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_300 (Dense) (None, 32) 2080 dense_299[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_300[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_300[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_990 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_990 ( [()] 0 tf_op_layer_Shape_990[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_99 (TensorFlow [(2,)] 0 tf_op_layer_strided_slice_990[0][
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_99[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_297 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_99 (TensorFlowO [(None, 3)] 0 tf_op_layer_Mul_297[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_991 (TensorFl [(2,)] 0 tf_op_layer_Add_99[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_993 (TensorFl [(2,)] 0 tf_op_layer_Add_99[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_992 (TensorFl [(2,)] 0 tf_op_layer_Add_99[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_994 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_996 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_995 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_997 (TensorFl [(2,)] 0 tf_op_layer_Add_99[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_999 (TensorFl [(2,)] 0 tf_op_layer_Add_99[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_998 (TensorFl [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_991 ( [()] 0 tf_op_layer_Shape_991[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_993 ( [()] 0 tf_op_layer_Shape_993[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_992 ( [()] 0 tf_op_layer_Shape_992[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_994 ( [()] 0 tf_op_layer_Shape_994[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_996 ( [()] 0 tf_op_layer_Shape_996[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_995 ( [()] 0 tf_op_layer_Shape_995[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_997 ( [()] 0 tf_op_layer_Shape_997[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_999 ( [()] 0 tf_op_layer_Shape_999[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_998 ( [()] 0 tf_op_layer_Shape_998[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_594/shape ( [(3,)] 0 tf_op_layer_strided_slice_991[0][
tf_op_layer_strided_slice_993[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_595/shape ( [(3,)] 0 tf_op_layer_strided_slice_992[0][
tf_op_layer_strided_slice_993[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_596/shape ( [(3,)] 0 tf_op_layer_strided_slice_994[0][
tf_op_layer_strided_slice_996[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_597/shape ( [(3,)] 0 tf_op_layer_strided_slice_995[0][
tf_op_layer_strided_slice_996[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_598/shape ( [(3,)] 0 tf_op_layer_strided_slice_997[0][
tf_op_layer_strided_slice_999[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_599/shape ( [(3,)] 0 tf_op_layer_strided_slice_998[0][
tf_op_layer_strided_slice_999[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_594 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_99[0][0]
tf_op_layer_Reshape_594/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_594/multiples [(3,)] 0 tf_op_layer_strided_slice_992[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_595 (Tensor [(1, None, None)] 0 tf_op_layer_Add_99[0][0]
tf_op_layer_Reshape_595/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_595/multiples [(3,)] 0 tf_op_layer_strided_slice_991[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_596 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_596/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_596/multiples [(3,)] 0 tf_op_layer_strided_slice_995[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_597 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_597/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_597/multiples [(3,)] 0 tf_op_layer_strided_slice_994[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_598 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_99[0][0]
tf_op_layer_Reshape_598/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_598/multiples [(3,)] 0 tf_op_layer_strided_slice_998[0][
__________________________________________________________________________________________________
tf_op_layer_Reshape_599 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_599/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_599/multiples [(3,)] 0 tf_op_layer_strided_slice_997[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_594 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_594[0][0]
tf_op_layer_Tile_594/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_595 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_595[0][0]
tf_op_layer_Tile_595/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_596 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_596[0][0]
tf_op_layer_Tile_596/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_597 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_597[0][0]
tf_op_layer_Tile_597/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_598 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_598[0][0]
tf_op_layer_Tile_598/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_599 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_599[0][0]
tf_op_layer_Tile_599/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_495 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_594[0][0]
tf_op_layer_Tile_595[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_496 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_596[0][0]
tf_op_layer_Tile_597[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_497 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_598[0][0]
tf_op_layer_Tile_599[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_396 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_495[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_397 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_496[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_398 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_497[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_693 (TensorFlo [(None, None)] 0 tf_op_layer_Square_396[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_694 (TensorFlo [(None, None)] 0 tf_op_layer_Square_397[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_695 (TensorFlo [(None, None)] 0 tf_op_layer_Square_398[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_297 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_693[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_297 (TensorFlo [()] 0 tf_op_layer_strided_slice_993[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_298 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_694[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_298 (TensorFlo [()] 0 tf_op_layer_strided_slice_996[0][
__________________________________________________________________________________________________
tf_op_layer_Neg_299 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_695[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_299 (TensorFlo [()] 0 tf_op_layer_strided_slice_999[0][
__________________________________________________________________________________________________
tf_op_layer_RealDiv_297 (Tensor [(None, None)] 0 tf_op_layer_Neg_297[0][0]
tf_op_layer_Cast_297[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_298 (Tensor [(None, None)] 0 tf_op_layer_Neg_298[0][0]
tf_op_layer_Cast_298[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_299 (Tensor [(None, None)] 0 tf_op_layer_Neg_299[0][0]
tf_op_layer_Cast_299[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_297 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_297[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_298 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_298[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_299 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_299[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_696 (TensorFlo [()] 0 tf_op_layer_Exp_297[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_697 (TensorFlo [()] 0 tf_op_layer_Exp_298[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_698 (TensorFlo [()] 0 tf_op_layer_Exp_299[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_499 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_198 (TensorFl [()] 0 tf_op_layer_Mean_696[0][0]
tf_op_layer_Mean_697[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_298 (TensorFlow [()] 0 tf_op_layer_Mean_698[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_399 (TensorF [(None, 97)] 0 tf_op_layer_Sub_499[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_498 (TensorFlow [()] 0 tf_op_layer_AddV2_198[0][0]
tf_op_layer_Mul_298[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_99 (TensorFlowO [(None,)] 0 tf_op_layer_Square_399[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_299 (TensorFlow [()] 0 tf_op_layer_Sub_498[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_199 (TensorFl [(None,)] 0 tf_op_layer_Sum_99[0][0]
tf_op_layer_Mul_299[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_699 (TensorFlo [()] 0 tf_op_layer_AddV2_199[0][0]
__________________________________________________________________________________________________
add_loss_99 (AddLoss) () 0 tf_op_layer_Mean_699[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.2354WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0940s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9047 - val_loss: 0.3998
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4033 - val_loss: 0.3770
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3816 - val_loss: 0.3649
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3745 - val_loss: 0.3640
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3687 - val_loss: 0.3555
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3672 - val_loss: 0.3485
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3636 - val_loss: 0.3485
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3608 - val_loss: 0.3512
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3625 - val_loss: 0.3456
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3586 - val_loss: 0.3438
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3571 - val_loss: 0.3396
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3564 - val_loss: 0.3416
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3557 - val_loss: 0.3419
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3548 - val_loss: 0.3418
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3394
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3531 - val_loss: 0.3415
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3530 - val_loss: 0.3404
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3531 - val_loss: 0.3412
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3514 - val_loss: 0.3379
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3517 - val_loss: 0.3387
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3405
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3508 - val_loss: 0.3383
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3500 - val_loss: 0.3378
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3496 - val_loss: 0.3369
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3483 - val_loss: 0.3365
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3363
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3364
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3372
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3343
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3364
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3351
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3365
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3352
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3340
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3474 - val_loss: 0.3337
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3354
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3337
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3455 - val_loss: 0.3343
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3366
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3327
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3353
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3336
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3344
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3350
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3317
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3321
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3322
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3335
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3322
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3318
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3310
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3331
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3316
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3335
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3351
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3322
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3325
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3310
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3353
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3323
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3312
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3296
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3315
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3308
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3300
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3311
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3346
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3332
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3308
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3325
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3320
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3322
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3308
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3376
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3318
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3314
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3316
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3313
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3307
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3308
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3310
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3317
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3307
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3291
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3309
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3303
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3296
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3293
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3318
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3266
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3300
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3304
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3339
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3299
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3286
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3327
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3309
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3303
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3302
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3298
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_304 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1000 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1000 [()] 0 tf_op_layer_Shape_1000[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_100 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1000[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_100[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_300 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_100 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_300[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1001 (TensorF [(2,)] 0 tf_op_layer_Add_100[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1003 (TensorF [(2,)] 0 tf_op_layer_Add_100[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1002 (TensorF [(2,)] 0 tf_op_layer_Add_100[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1004 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1006 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1005 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1007 (TensorF [(2,)] 0 tf_op_layer_Add_100[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1009 (TensorF [(2,)] 0 tf_op_layer_Add_100[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1008 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1001 [()] 0 tf_op_layer_Shape_1001[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1003 [()] 0 tf_op_layer_Shape_1003[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1002 [()] 0 tf_op_layer_Shape_1002[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1004 [()] 0 tf_op_layer_Shape_1004[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1006 [()] 0 tf_op_layer_Shape_1006[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1005 [()] 0 tf_op_layer_Shape_1005[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1007 [()] 0 tf_op_layer_Shape_1007[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1009 [()] 0 tf_op_layer_Shape_1009[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1008 [()] 0 tf_op_layer_Shape_1008[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_600/shape ( [(3,)] 0 tf_op_layer_strided_slice_1001[0]
tf_op_layer_strided_slice_1003[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_601/shape ( [(3,)] 0 tf_op_layer_strided_slice_1002[0]
tf_op_layer_strided_slice_1003[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_602/shape ( [(3,)] 0 tf_op_layer_strided_slice_1004[0]
tf_op_layer_strided_slice_1006[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_603/shape ( [(3,)] 0 tf_op_layer_strided_slice_1005[0]
tf_op_layer_strided_slice_1006[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_604/shape ( [(3,)] 0 tf_op_layer_strided_slice_1007[0]
tf_op_layer_strided_slice_1009[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_605/shape ( [(3,)] 0 tf_op_layer_strided_slice_1008[0]
tf_op_layer_strided_slice_1009[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_600 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_100[0][0]
tf_op_layer_Reshape_600/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_600/multiples [(3,)] 0 tf_op_layer_strided_slice_1002[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_601 (Tensor [(1, None, None)] 0 tf_op_layer_Add_100[0][0]
tf_op_layer_Reshape_601/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_601/multiples [(3,)] 0 tf_op_layer_strided_slice_1001[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_602 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_602/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_602/multiples [(3,)] 0 tf_op_layer_strided_slice_1005[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_603 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_603/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_603/multiples [(3,)] 0 tf_op_layer_strided_slice_1004[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_604 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_100[0][0]
tf_op_layer_Reshape_604/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_604/multiples [(3,)] 0 tf_op_layer_strided_slice_1008[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_605 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_605/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_605/multiples [(3,)] 0 tf_op_layer_strided_slice_1007[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_600 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_600[0][0]
tf_op_layer_Tile_600/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_601 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_601[0][0]
tf_op_layer_Tile_601/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_602 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_602[0][0]
tf_op_layer_Tile_602/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_603 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_603[0][0]
tf_op_layer_Tile_603/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_604 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_604[0][0]
tf_op_layer_Tile_604/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_605 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_605[0][0]
tf_op_layer_Tile_605/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_500 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_600[0][0]
tf_op_layer_Tile_601[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_501 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_602[0][0]
tf_op_layer_Tile_603[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_502 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_604[0][0]
tf_op_layer_Tile_605[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_400 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_500[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_401 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_501[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_402 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_502[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_700 (TensorFlo [(None, None)] 0 tf_op_layer_Square_400[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_701 (TensorFlo [(None, None)] 0 tf_op_layer_Square_401[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_702 (TensorFlo [(None, None)] 0 tf_op_layer_Square_402[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_300 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_700[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_300 (TensorFlo [()] 0 tf_op_layer_strided_slice_1003[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_301 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_701[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_301 (TensorFlo [()] 0 tf_op_layer_strided_slice_1006[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_302 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_702[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_302 (TensorFlo [()] 0 tf_op_layer_strided_slice_1009[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_300 (Tensor [(None, None)] 0 tf_op_layer_Neg_300[0][0]
tf_op_layer_Cast_300[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_301 (Tensor [(None, None)] 0 tf_op_layer_Neg_301[0][0]
tf_op_layer_Cast_301[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_302 (Tensor [(None, None)] 0 tf_op_layer_Neg_302[0][0]
tf_op_layer_Cast_302[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_300 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_300[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_301 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_301[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_302 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_302[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_703 (TensorFlo [()] 0 tf_op_layer_Exp_300[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_704 (TensorFlo [()] 0 tf_op_layer_Exp_301[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_705 (TensorFlo [()] 0 tf_op_layer_Exp_302[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_504 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_200 (TensorFl [()] 0 tf_op_layer_Mean_703[0][0]
tf_op_layer_Mean_704[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_301 (TensorFlow [()] 0 tf_op_layer_Mean_705[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_403 (TensorF [(None, 97)] 0 tf_op_layer_Sub_504[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_503 (TensorFlow [()] 0 tf_op_layer_AddV2_200[0][0]
tf_op_layer_Mul_301[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_100 (TensorFlow [(None,)] 0 tf_op_layer_Square_403[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_302 (TensorFlow [()] 0 tf_op_layer_Sub_503[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_201 (TensorFl [(None,)] 0 tf_op_layer_Sum_100[0][0]
tf_op_layer_Mul_302[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_706 (TensorFlo [()] 0 tf_op_layer_AddV2_201[0][0]
__________________________________________________________________________________________________
add_loss_100 (AddLoss) () 0 tf_op_layer_Mean_706[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 7.6820WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.1054s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9499 - val_loss: 0.9601
Epoch 2/100
218/218 [==============================] - 0s 954us/step - loss: 0.8203 - val_loss: 0.7794
Epoch 3/100
218/218 [==============================] - 0s 958us/step - loss: 0.6954 - val_loss: 0.6929
Epoch 4/100
218/218 [==============================] - 0s 962us/step - loss: 0.6388 - val_loss: 0.6546
Epoch 5/100
218/218 [==============================] - 0s 952us/step - loss: 0.6076 - val_loss: 0.6248
Epoch 6/100
218/218 [==============================] - 0s 958us/step - loss: 0.5818 - val_loss: 0.6054
Epoch 7/100
218/218 [==============================] - 0s 947us/step - loss: 0.5715 - val_loss: 0.5898
Epoch 8/100
218/218 [==============================] - 0s 954us/step - loss: 0.5562 - val_loss: 0.5825
Epoch 9/100
218/218 [==============================] - 0s 961us/step - loss: 0.5459 - val_loss: 0.5770
Epoch 10/100
218/218 [==============================] - 0s 955us/step - loss: 0.5389 - val_loss: 0.5722
Epoch 11/100
218/218 [==============================] - 0s 956us/step - loss: 0.5331 - val_loss: 0.5599
Epoch 12/100
218/218 [==============================] - 0s 954us/step - loss: 0.5273 - val_loss: 0.5604
Epoch 13/100
218/218 [==============================] - 0s 952us/step - loss: 0.5250 - val_loss: 0.5526
Epoch 14/100
218/218 [==============================] - 0s 952us/step - loss: 0.5163 - val_loss: 0.5535
Epoch 15/100
218/218 [==============================] - 0s 951us/step - loss: 0.5159 - val_loss: 0.5410
Epoch 16/100
218/218 [==============================] - 0s 959us/step - loss: 0.5119 - val_loss: 0.5327
Epoch 17/100
218/218 [==============================] - 0s 955us/step - loss: 0.5078 - val_loss: 0.5367
Epoch 18/100
218/218 [==============================] - 0s 947us/step - loss: 0.5033 - val_loss: 0.5322
Epoch 19/100
218/218 [==============================] - 0s 946us/step - loss: 0.5027 - val_loss: 0.5282
Epoch 20/100
218/218 [==============================] - 0s 953us/step - loss: 0.4986 - val_loss: 0.5262
Epoch 21/100
218/218 [==============================] - 0s 954us/step - loss: 0.4959 - val_loss: 0.5255
Epoch 22/100
218/218 [==============================] - 0s 951us/step - loss: 0.4958 - val_loss: 0.5277
Epoch 23/100
218/218 [==============================] - 0s 949us/step - loss: 0.4920 - val_loss: 0.5186
Epoch 24/100
218/218 [==============================] - 0s 950us/step - loss: 0.4913 - val_loss: 0.5159
Epoch 25/100
218/218 [==============================] - 0s 967us/step - loss: 0.4883 - val_loss: 0.5180
Epoch 26/100
218/218 [==============================] - 0s 946us/step - loss: 0.4891 - val_loss: 0.5185
Epoch 27/100
218/218 [==============================] - 0s 952us/step - loss: 0.4874 - val_loss: 0.5179
Epoch 28/100
218/218 [==============================] - 0s 953us/step - loss: 0.4845 - val_loss: 0.5179
Epoch 29/100
218/218 [==============================] - 0s 952us/step - loss: 0.4849 - val_loss: 0.5180
Epoch 30/100
218/218 [==============================] - 0s 956us/step - loss: 0.4829 - val_loss: 0.5144
Epoch 31/100
218/218 [==============================] - 0s 952us/step - loss: 0.4807 - val_loss: 0.5111
Epoch 32/100
218/218 [==============================] - 0s 948us/step - loss: 0.4840 - val_loss: 0.5150
Epoch 33/100
218/218 [==============================] - 0s 953us/step - loss: 0.4788 - val_loss: 0.5089
Epoch 34/100
218/218 [==============================] - 0s 955us/step - loss: 0.4781 - val_loss: 0.5085
Epoch 35/100
218/218 [==============================] - 0s 962us/step - loss: 0.4769 - val_loss: 0.5102
Epoch 36/100
218/218 [==============================] - 0s 945us/step - loss: 0.4778 - val_loss: 0.5084
Epoch 37/100
218/218 [==============================] - 0s 950us/step - loss: 0.4755 - val_loss: 0.5047
Epoch 38/100
218/218 [==============================] - 0s 966us/step - loss: 0.4755 - val_loss: 0.5127
Epoch 39/100
218/218 [==============================] - 0s 951us/step - loss: 0.4745 - val_loss: 0.5060
Epoch 40/100
218/218 [==============================] - 0s 948us/step - loss: 0.4725 - val_loss: 0.5020
Epoch 41/100
218/218 [==============================] - 0s 959us/step - loss: 0.4738 - val_loss: 0.5030
Epoch 42/100
218/218 [==============================] - 0s 957us/step - loss: 0.4736 - val_loss: 0.5052
Epoch 43/100
218/218 [==============================] - 0s 947us/step - loss: 0.4732 - val_loss: 0.4980
Epoch 44/100
218/218 [==============================] - 0s 952us/step - loss: 0.4718 - val_loss: 0.5024
Epoch 45/100
218/218 [==============================] - 0s 948us/step - loss: 0.4717 - val_loss: 0.4990
Epoch 46/100
218/218 [==============================] - 0s 952us/step - loss: 0.4721 - val_loss: 0.4996
Epoch 47/100
218/218 [==============================] - 0s 947us/step - loss: 0.4676 - val_loss: 0.5017
Epoch 48/100
218/218 [==============================] - 0s 953us/step - loss: 0.4698 - val_loss: 0.5050
Epoch 49/100
218/218 [==============================] - 0s 948us/step - loss: 0.4680 - val_loss: 0.4991
Epoch 50/100
218/218 [==============================] - 0s 946us/step - loss: 0.4690 - val_loss: 0.4959
Epoch 51/100
218/218 [==============================] - 0s 948us/step - loss: 0.4694 - val_loss: 0.5010
Epoch 52/100
218/218 [==============================] - 0s 947us/step - loss: 0.4675 - val_loss: 0.4969
Epoch 53/100
218/218 [==============================] - 0s 945us/step - loss: 0.4661 - val_loss: 0.4955
Epoch 54/100
218/218 [==============================] - 0s 950us/step - loss: 0.4664 - val_loss: 0.4971
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4667 - val_loss: 0.4963
Epoch 56/100
218/218 [==============================] - 0s 983us/step - loss: 0.4655 - val_loss: 0.4958
Epoch 57/100
218/218 [==============================] - 0s 978us/step - loss: 0.4655 - val_loss: 0.4938
Epoch 58/100
218/218 [==============================] - 0s 971us/step - loss: 0.4665 - val_loss: 0.4991
Epoch 59/100
218/218 [==============================] - 0s 976us/step - loss: 0.4660 - val_loss: 0.5001
Epoch 60/100
218/218 [==============================] - 0s 965us/step - loss: 0.4647 - val_loss: 0.4996
Epoch 61/100
218/218 [==============================] - 0s 950us/step - loss: 0.4655 - val_loss: 0.5005
Epoch 62/100
218/218 [==============================] - 0s 953us/step - loss: 0.4676 - val_loss: 0.4965
Epoch 63/100
218/218 [==============================] - 0s 964us/step - loss: 0.4640 - val_loss: 0.4921
Epoch 64/100
218/218 [==============================] - 0s 971us/step - loss: 0.4638 - val_loss: 0.4981
Epoch 65/100
218/218 [==============================] - 0s 967us/step - loss: 0.4638 - val_loss: 0.4874
Epoch 66/100
218/218 [==============================] - 0s 960us/step - loss: 0.4641 - val_loss: 0.4955
Epoch 67/100
218/218 [==============================] - 0s 955us/step - loss: 0.4638 - val_loss: 0.4936
Epoch 68/100
218/218 [==============================] - 0s 961us/step - loss: 0.4643 - val_loss: 0.4909
Epoch 69/100
218/218 [==============================] - 0s 952us/step - loss: 0.4637 - val_loss: 0.4932
Epoch 70/100
218/218 [==============================] - 0s 962us/step - loss: 0.4621 - val_loss: 0.4923
Epoch 71/100
218/218 [==============================] - 0s 962us/step - loss: 0.4615 - val_loss: 0.4943
Epoch 72/100
218/218 [==============================] - 0s 981us/step - loss: 0.4631 - val_loss: 0.4930
Epoch 73/100
218/218 [==============================] - 0s 972us/step - loss: 0.4626 - val_loss: 0.4910
Epoch 74/100
218/218 [==============================] - 0s 954us/step - loss: 0.4613 - val_loss: 0.4919
Epoch 75/100
218/218 [==============================] - 0s 959us/step - loss: 0.4617 - val_loss: 0.4891
Epoch 76/100
218/218 [==============================] - 0s 962us/step - loss: 0.4604 - val_loss: 0.4929
Epoch 77/100
218/218 [==============================] - 0s 960us/step - loss: 0.4590 - val_loss: 0.4916
Epoch 78/100
218/218 [==============================] - 0s 952us/step - loss: 0.4596 - val_loss: 0.4895
Epoch 79/100
218/218 [==============================] - 0s 948us/step - loss: 0.4586 - val_loss: 0.4884
Epoch 80/100
218/218 [==============================] - 0s 955us/step - loss: 0.4626 - val_loss: 0.4929
Epoch 81/100
218/218 [==============================] - 0s 962us/step - loss: 0.4599 - val_loss: 0.4926
Epoch 82/100
218/218 [==============================] - 0s 958us/step - loss: 0.4590 - val_loss: 0.4897
Epoch 83/100
218/218 [==============================] - 0s 956us/step - loss: 0.4594 - val_loss: 0.4932
Epoch 84/100
218/218 [==============================] - 0s 958us/step - loss: 0.4597 - val_loss: 0.4894
Epoch 85/100
218/218 [==============================] - 0s 957us/step - loss: 0.4606 - val_loss: 0.4908
Epoch 86/100
218/218 [==============================] - 0s 954us/step - loss: 0.4591 - val_loss: 0.4900
Epoch 87/100
218/218 [==============================] - 0s 965us/step - loss: 0.4587 - val_loss: 0.4865
Epoch 88/100
218/218 [==============================] - 0s 966us/step - loss: 0.4596 - val_loss: 0.4884
Epoch 89/100
218/218 [==============================] - 0s 970us/step - loss: 0.4587 - val_loss: 0.4905
Epoch 90/100
218/218 [==============================] - 0s 958us/step - loss: 0.4589 - val_loss: 0.4913
Epoch 91/100
218/218 [==============================] - 0s 956us/step - loss: 0.4586 - val_loss: 0.4880
Epoch 92/100
218/218 [==============================] - 0s 953us/step - loss: 0.4589 - val_loss: 0.4900
Epoch 93/100
218/218 [==============================] - 0s 955us/step - loss: 0.4569 - val_loss: 0.4897
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4590 - val_loss: 0.4925
Epoch 95/100
218/218 [==============================] - 0s 956us/step - loss: 0.4581 - val_loss: 0.4865
Epoch 96/100
218/218 [==============================] - 0s 961us/step - loss: 0.4582 - val_loss: 0.4848
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4576 - val_loss: 0.4862
Epoch 98/100
218/218 [==============================] - 0s 957us/step - loss: 0.4561 - val_loss: 0.4870
Epoch 99/100
218/218 [==============================] - 0s 958us/step - loss: 0.4564 - val_loss: 0.4883
Epoch 100/100
218/218 [==============================] - 0s 958us/step - loss: 0.4566 - val_loss: 0.4862
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.69 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.96 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.88 seconds.
Calculating KMeans...
Calculated KMeans in 14.71 seconds.
Calculated landmark operator in 17.05 seconds.
Calculating optimal t...
Automatically selected t = 42
Calculated optimal t in 2.89 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.62 seconds.
Calculating metric MDS...
Calculated metric MDS in 17.43 seconds.
Calculated PHATE in 51.96 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_305 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_306 (Dense) (None, 32) 2080 dense_305[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_306[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_306[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_307 (Dense) (None, 32) 128
_________________________________________________________________
dense_308 (Dense) (None, 64) 2112
_________________________________________________________________
dense_309 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_305 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_306 (Dense) (None, 32) 2080 dense_305[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_306[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_306[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1010 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1010 [()] 0 tf_op_layer_Shape_1010[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_101 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1010[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_101[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_303 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_101 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_303[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1011 (TensorF [(2,)] 0 tf_op_layer_Add_101[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1013 (TensorF [(2,)] 0 tf_op_layer_Add_101[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1012 (TensorF [(2,)] 0 tf_op_layer_Add_101[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1014 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1016 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1015 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1017 (TensorF [(2,)] 0 tf_op_layer_Add_101[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1019 (TensorF [(2,)] 0 tf_op_layer_Add_101[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1018 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1011 [()] 0 tf_op_layer_Shape_1011[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1013 [()] 0 tf_op_layer_Shape_1013[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1012 [()] 0 tf_op_layer_Shape_1012[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1014 [()] 0 tf_op_layer_Shape_1014[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1016 [()] 0 tf_op_layer_Shape_1016[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1015 [()] 0 tf_op_layer_Shape_1015[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1017 [()] 0 tf_op_layer_Shape_1017[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1019 [()] 0 tf_op_layer_Shape_1019[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1018 [()] 0 tf_op_layer_Shape_1018[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_606/shape ( [(3,)] 0 tf_op_layer_strided_slice_1011[0]
tf_op_layer_strided_slice_1013[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_607/shape ( [(3,)] 0 tf_op_layer_strided_slice_1012[0]
tf_op_layer_strided_slice_1013[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_608/shape ( [(3,)] 0 tf_op_layer_strided_slice_1014[0]
tf_op_layer_strided_slice_1016[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_609/shape ( [(3,)] 0 tf_op_layer_strided_slice_1015[0]
tf_op_layer_strided_slice_1016[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_610/shape ( [(3,)] 0 tf_op_layer_strided_slice_1017[0]
tf_op_layer_strided_slice_1019[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_611/shape ( [(3,)] 0 tf_op_layer_strided_slice_1018[0]
tf_op_layer_strided_slice_1019[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_606 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_101[0][0]
tf_op_layer_Reshape_606/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_606/multiples [(3,)] 0 tf_op_layer_strided_slice_1012[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_607 (Tensor [(1, None, None)] 0 tf_op_layer_Add_101[0][0]
tf_op_layer_Reshape_607/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_607/multiples [(3,)] 0 tf_op_layer_strided_slice_1011[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_608 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_608/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_608/multiples [(3,)] 0 tf_op_layer_strided_slice_1015[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_609 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_609/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_609/multiples [(3,)] 0 tf_op_layer_strided_slice_1014[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_610 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_101[0][0]
tf_op_layer_Reshape_610/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_610/multiples [(3,)] 0 tf_op_layer_strided_slice_1018[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_611 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_611/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_611/multiples [(3,)] 0 tf_op_layer_strided_slice_1017[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_606 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_606[0][0]
tf_op_layer_Tile_606/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_607 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_607[0][0]
tf_op_layer_Tile_607/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_608 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_608[0][0]
tf_op_layer_Tile_608/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_609 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_609[0][0]
tf_op_layer_Tile_609/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_610 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_610[0][0]
tf_op_layer_Tile_610/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_611 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_611[0][0]
tf_op_layer_Tile_611/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_505 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_606[0][0]
tf_op_layer_Tile_607[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_506 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_608[0][0]
tf_op_layer_Tile_609[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_507 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_610[0][0]
tf_op_layer_Tile_611[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_404 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_505[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_405 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_506[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_406 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_507[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_707 (TensorFlo [(None, None)] 0 tf_op_layer_Square_404[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_708 (TensorFlo [(None, None)] 0 tf_op_layer_Square_405[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_709 (TensorFlo [(None, None)] 0 tf_op_layer_Square_406[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_303 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_707[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_303 (TensorFlo [()] 0 tf_op_layer_strided_slice_1013[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_304 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_708[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_304 (TensorFlo [()] 0 tf_op_layer_strided_slice_1016[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_305 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_709[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_305 (TensorFlo [()] 0 tf_op_layer_strided_slice_1019[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_303 (Tensor [(None, None)] 0 tf_op_layer_Neg_303[0][0]
tf_op_layer_Cast_303[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_304 (Tensor [(None, None)] 0 tf_op_layer_Neg_304[0][0]
tf_op_layer_Cast_304[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_305 (Tensor [(None, None)] 0 tf_op_layer_Neg_305[0][0]
tf_op_layer_Cast_305[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_303 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_303[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_304 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_304[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_305 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_305[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_710 (TensorFlo [()] 0 tf_op_layer_Exp_303[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_711 (TensorFlo [()] 0 tf_op_layer_Exp_304[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_712 (TensorFlo [()] 0 tf_op_layer_Exp_305[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_509 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_202 (TensorFl [()] 0 tf_op_layer_Mean_710[0][0]
tf_op_layer_Mean_711[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_304 (TensorFlow [()] 0 tf_op_layer_Mean_712[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_407 (TensorF [(None, 97)] 0 tf_op_layer_Sub_509[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_508 (TensorFlow [()] 0 tf_op_layer_AddV2_202[0][0]
tf_op_layer_Mul_304[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_101 (TensorFlow [(None,)] 0 tf_op_layer_Square_407[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_305 (TensorFlow [()] 0 tf_op_layer_Sub_508[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_203 (TensorFl [(None,)] 0 tf_op_layer_Sum_101[0][0]
tf_op_layer_Mul_305[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_713 (TensorFlo [()] 0 tf_op_layer_AddV2_203[0][0]
__________________________________________________________________________________________________
add_loss_101 (AddLoss) () 0 tf_op_layer_Mean_713[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 7.4427WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1045s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9630 - val_loss: 0.4195
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4017 - val_loss: 0.3887
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3872 - val_loss: 0.3814
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3786 - val_loss: 0.3733
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3726 - val_loss: 0.3690
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3679 - val_loss: 0.3615
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3669 - val_loss: 0.3626
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3641 - val_loss: 0.3602
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3624 - val_loss: 0.3588
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3620 - val_loss: 0.3549
Epoch 11/100
218/218 [==============================] - 0s 955us/step - loss: 0.3599 - val_loss: 0.3580
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3580 - val_loss: 0.3559
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3584 - val_loss: 0.3562
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3568 - val_loss: 0.3556
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3554 - val_loss: 0.3518
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3569 - val_loss: 0.3543
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3542 - val_loss: 0.3519
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3526 - val_loss: 0.3518
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3534 - val_loss: 0.3489
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3534 - val_loss: 0.3545
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3543 - val_loss: 0.3505
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3529 - val_loss: 0.3529
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3514 - val_loss: 0.3538
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3517 - val_loss: 0.3499
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3518 - val_loss: 0.3512
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3511 - val_loss: 0.3476
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3510 - val_loss: 0.3497
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3488
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3479
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3492 - val_loss: 0.3492
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3494
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3498
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3505 - val_loss: 0.3479
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3500 - val_loss: 0.3471
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3456
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3490
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3465
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3467
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3492 - val_loss: 0.3469
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3478
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3462
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3453
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3467
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3480
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3460
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3458
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3468
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3470
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3467
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3470
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3440
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3474 - val_loss: 0.3466
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3445
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3437
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3434
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3462
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3452
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3438
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3429
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3460
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3445
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3451
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3424
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3450
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3427
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3436
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3439
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3434
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3416
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3427
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3435
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3434
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3462
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3411
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3423
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3446
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3432
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3439
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3439
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3419
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3428
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3450
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3409
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3440
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3439
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3425
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3437
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3424
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3430
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3438
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3443
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3444
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3426
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3423
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3422
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3448
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3422
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3430
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3409
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3401
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_310 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1020 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1020 [()] 0 tf_op_layer_Shape_1020[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_102 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1020[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_102[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_306 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_102 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_306[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1021 (TensorF [(2,)] 0 tf_op_layer_Add_102[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1023 (TensorF [(2,)] 0 tf_op_layer_Add_102[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1022 (TensorF [(2,)] 0 tf_op_layer_Add_102[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1024 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1026 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1025 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1027 (TensorF [(2,)] 0 tf_op_layer_Add_102[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1029 (TensorF [(2,)] 0 tf_op_layer_Add_102[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1028 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1021 [()] 0 tf_op_layer_Shape_1021[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1023 [()] 0 tf_op_layer_Shape_1023[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1022 [()] 0 tf_op_layer_Shape_1022[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1024 [()] 0 tf_op_layer_Shape_1024[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1026 [()] 0 tf_op_layer_Shape_1026[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1025 [()] 0 tf_op_layer_Shape_1025[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1027 [()] 0 tf_op_layer_Shape_1027[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1029 [()] 0 tf_op_layer_Shape_1029[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1028 [()] 0 tf_op_layer_Shape_1028[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_612/shape ( [(3,)] 0 tf_op_layer_strided_slice_1021[0]
tf_op_layer_strided_slice_1023[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_613/shape ( [(3,)] 0 tf_op_layer_strided_slice_1022[0]
tf_op_layer_strided_slice_1023[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_614/shape ( [(3,)] 0 tf_op_layer_strided_slice_1024[0]
tf_op_layer_strided_slice_1026[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_615/shape ( [(3,)] 0 tf_op_layer_strided_slice_1025[0]
tf_op_layer_strided_slice_1026[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_616/shape ( [(3,)] 0 tf_op_layer_strided_slice_1027[0]
tf_op_layer_strided_slice_1029[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_617/shape ( [(3,)] 0 tf_op_layer_strided_slice_1028[0]
tf_op_layer_strided_slice_1029[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_612 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_102[0][0]
tf_op_layer_Reshape_612/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_612/multiples [(3,)] 0 tf_op_layer_strided_slice_1022[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_613 (Tensor [(1, None, None)] 0 tf_op_layer_Add_102[0][0]
tf_op_layer_Reshape_613/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_613/multiples [(3,)] 0 tf_op_layer_strided_slice_1021[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_614 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_614/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_614/multiples [(3,)] 0 tf_op_layer_strided_slice_1025[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_615 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_615/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_615/multiples [(3,)] 0 tf_op_layer_strided_slice_1024[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_616 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_102[0][0]
tf_op_layer_Reshape_616/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_616/multiples [(3,)] 0 tf_op_layer_strided_slice_1028[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_617 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_617/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_617/multiples [(3,)] 0 tf_op_layer_strided_slice_1027[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_612 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_612[0][0]
tf_op_layer_Tile_612/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_613 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_613[0][0]
tf_op_layer_Tile_613/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_614 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_614[0][0]
tf_op_layer_Tile_614/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_615 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_615[0][0]
tf_op_layer_Tile_615/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_616 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_616[0][0]
tf_op_layer_Tile_616/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_617 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_617[0][0]
tf_op_layer_Tile_617/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_510 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_612[0][0]
tf_op_layer_Tile_613[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_511 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_614[0][0]
tf_op_layer_Tile_615[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_512 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_616[0][0]
tf_op_layer_Tile_617[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_408 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_510[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_409 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_511[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_410 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_512[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_714 (TensorFlo [(None, None)] 0 tf_op_layer_Square_408[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_715 (TensorFlo [(None, None)] 0 tf_op_layer_Square_409[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_716 (TensorFlo [(None, None)] 0 tf_op_layer_Square_410[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_306 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_714[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_306 (TensorFlo [()] 0 tf_op_layer_strided_slice_1023[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_307 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_715[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_307 (TensorFlo [()] 0 tf_op_layer_strided_slice_1026[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_308 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_716[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_308 (TensorFlo [()] 0 tf_op_layer_strided_slice_1029[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_306 (Tensor [(None, None)] 0 tf_op_layer_Neg_306[0][0]
tf_op_layer_Cast_306[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_307 (Tensor [(None, None)] 0 tf_op_layer_Neg_307[0][0]
tf_op_layer_Cast_307[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_308 (Tensor [(None, None)] 0 tf_op_layer_Neg_308[0][0]
tf_op_layer_Cast_308[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_306 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_306[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_307 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_307[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_308 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_308[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_717 (TensorFlo [()] 0 tf_op_layer_Exp_306[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_718 (TensorFlo [()] 0 tf_op_layer_Exp_307[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_719 (TensorFlo [()] 0 tf_op_layer_Exp_308[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_514 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_204 (TensorFl [()] 0 tf_op_layer_Mean_717[0][0]
tf_op_layer_Mean_718[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_307 (TensorFlow [()] 0 tf_op_layer_Mean_719[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_411 (TensorF [(None, 97)] 0 tf_op_layer_Sub_514[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_513 (TensorFlow [()] 0 tf_op_layer_AddV2_204[0][0]
tf_op_layer_Mul_307[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_102 (TensorFlow [(None,)] 0 tf_op_layer_Square_411[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_308 (TensorFlow [()] 0 tf_op_layer_Sub_513[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_205 (TensorFl [(None,)] 0 tf_op_layer_Sum_102[0][0]
tf_op_layer_Mul_308[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_720 (TensorFlo [()] 0 tf_op_layer_AddV2_205[0][0]
__________________________________________________________________________________________________
add_loss_102 (AddLoss) () 0 tf_op_layer_Mean_720[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.9476WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0934s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 2.0368 - val_loss: 0.9865
Epoch 2/100
218/218 [==============================] - 0s 956us/step - loss: 0.8444 - val_loss: 0.7574
Epoch 3/100
218/218 [==============================] - 0s 959us/step - loss: 0.7111 - val_loss: 0.6704
Epoch 4/100
218/218 [==============================] - 0s 959us/step - loss: 0.6480 - val_loss: 0.6219
Epoch 5/100
218/218 [==============================] - 0s 958us/step - loss: 0.6188 - val_loss: 0.6023
Epoch 6/100
218/218 [==============================] - 0s 964us/step - loss: 0.5958 - val_loss: 0.5942
Epoch 7/100
218/218 [==============================] - 0s 960us/step - loss: 0.5775 - val_loss: 0.5659
Epoch 8/100
218/218 [==============================] - 0s 959us/step - loss: 0.5645 - val_loss: 0.5570
Epoch 9/100
218/218 [==============================] - 0s 954us/step - loss: 0.5562 - val_loss: 0.5513
Epoch 10/100
218/218 [==============================] - 0s 959us/step - loss: 0.5501 - val_loss: 0.5388
Epoch 11/100
218/218 [==============================] - 0s 952us/step - loss: 0.5405 - val_loss: 0.5394
Epoch 12/100
218/218 [==============================] - 0s 959us/step - loss: 0.5359 - val_loss: 0.5268
Epoch 13/100
218/218 [==============================] - 0s 958us/step - loss: 0.5298 - val_loss: 0.5277
Epoch 14/100
218/218 [==============================] - 0s 968us/step - loss: 0.5244 - val_loss: 0.5160
Epoch 15/100
218/218 [==============================] - 0s 941us/step - loss: 0.5180 - val_loss: 0.5135
Epoch 16/100
218/218 [==============================] - 0s 959us/step - loss: 0.5143 - val_loss: 0.5145
Epoch 17/100
218/218 [==============================] - 0s 970us/step - loss: 0.5123 - val_loss: 0.5036
Epoch 18/100
218/218 [==============================] - 0s 966us/step - loss: 0.5101 - val_loss: 0.5025
Epoch 19/100
218/218 [==============================] - 0s 963us/step - loss: 0.5064 - val_loss: 0.5085
Epoch 20/100
218/218 [==============================] - 0s 960us/step - loss: 0.5046 - val_loss: 0.5039
Epoch 21/100
218/218 [==============================] - 0s 960us/step - loss: 0.5035 - val_loss: 0.4976
Epoch 22/100
218/218 [==============================] - 0s 954us/step - loss: 0.4993 - val_loss: 0.4989
Epoch 23/100
218/218 [==============================] - 0s 956us/step - loss: 0.4987 - val_loss: 0.4925
Epoch 24/100
218/218 [==============================] - 0s 952us/step - loss: 0.4973 - val_loss: 0.4891
Epoch 25/100
218/218 [==============================] - 0s 956us/step - loss: 0.4948 - val_loss: 0.4906
Epoch 26/100
218/218 [==============================] - 0s 957us/step - loss: 0.4925 - val_loss: 0.4919
Epoch 27/100
218/218 [==============================] - 0s 956us/step - loss: 0.4911 - val_loss: 0.4890
Epoch 28/100
218/218 [==============================] - 0s 957us/step - loss: 0.4896 - val_loss: 0.4845
Epoch 29/100
218/218 [==============================] - 0s 955us/step - loss: 0.4895 - val_loss: 0.4836
Epoch 30/100
218/218 [==============================] - 0s 954us/step - loss: 0.4865 - val_loss: 0.4822
Epoch 31/100
218/218 [==============================] - 0s 958us/step - loss: 0.4839 - val_loss: 0.4816
Epoch 32/100
218/218 [==============================] - 0s 956us/step - loss: 0.4837 - val_loss: 0.4816
Epoch 33/100
218/218 [==============================] - 0s 957us/step - loss: 0.4832 - val_loss: 0.4805
Epoch 34/100
218/218 [==============================] - 0s 953us/step - loss: 0.4825 - val_loss: 0.4764
Epoch 35/100
218/218 [==============================] - 0s 958us/step - loss: 0.4817 - val_loss: 0.4809
Epoch 36/100
218/218 [==============================] - 0s 963us/step - loss: 0.4839 - val_loss: 0.4745
Epoch 37/100
218/218 [==============================] - 0s 957us/step - loss: 0.4823 - val_loss: 0.4799
Epoch 38/100
218/218 [==============================] - 0s 958us/step - loss: 0.4806 - val_loss: 0.4781
Epoch 39/100
218/218 [==============================] - 0s 955us/step - loss: 0.4790 - val_loss: 0.4750
Epoch 40/100
218/218 [==============================] - 0s 959us/step - loss: 0.4779 - val_loss: 0.4767
Epoch 41/100
218/218 [==============================] - 0s 956us/step - loss: 0.4787 - val_loss: 0.4771
Epoch 42/100
218/218 [==============================] - 0s 955us/step - loss: 0.4778 - val_loss: 0.4737
Epoch 43/100
218/218 [==============================] - 0s 956us/step - loss: 0.4739 - val_loss: 0.4693
Epoch 44/100
218/218 [==============================] - 0s 959us/step - loss: 0.4757 - val_loss: 0.4763
Epoch 45/100
218/218 [==============================] - 0s 955us/step - loss: 0.4754 - val_loss: 0.4680
Epoch 46/100
218/218 [==============================] - 0s 953us/step - loss: 0.4750 - val_loss: 0.4707
Epoch 47/100
218/218 [==============================] - 0s 954us/step - loss: 0.4763 - val_loss: 0.4746
Epoch 48/100
218/218 [==============================] - 0s 954us/step - loss: 0.4732 - val_loss: 0.4715
Epoch 49/100
218/218 [==============================] - 0s 949us/step - loss: 0.4735 - val_loss: 0.4722
Epoch 50/100
218/218 [==============================] - 0s 957us/step - loss: 0.4742 - val_loss: 0.4699
Epoch 51/100
218/218 [==============================] - 0s 959us/step - loss: 0.4718 - val_loss: 0.4726
Epoch 52/100
218/218 [==============================] - 0s 964us/step - loss: 0.4722 - val_loss: 0.4678
Epoch 53/100
218/218 [==============================] - 0s 959us/step - loss: 0.4717 - val_loss: 0.4706
Epoch 54/100
218/218 [==============================] - 0s 951us/step - loss: 0.4723 - val_loss: 0.4712
Epoch 55/100
218/218 [==============================] - 0s 953us/step - loss: 0.4715 - val_loss: 0.4679
Epoch 56/100
218/218 [==============================] - 0s 949us/step - loss: 0.4699 - val_loss: 0.4734
Epoch 57/100
218/218 [==============================] - 0s 954us/step - loss: 0.4702 - val_loss: 0.4681
Epoch 58/100
218/218 [==============================] - 0s 956us/step - loss: 0.4704 - val_loss: 0.4645
Epoch 59/100
218/218 [==============================] - 0s 955us/step - loss: 0.4675 - val_loss: 0.4694
Epoch 60/100
218/218 [==============================] - 0s 960us/step - loss: 0.4689 - val_loss: 0.4655
Epoch 61/100
218/218 [==============================] - 0s 957us/step - loss: 0.4682 - val_loss: 0.4665
Epoch 62/100
218/218 [==============================] - 0s 952us/step - loss: 0.4704 - val_loss: 0.4646
Epoch 63/100
218/218 [==============================] - 0s 954us/step - loss: 0.4692 - val_loss: 0.4600
Epoch 64/100
218/218 [==============================] - 0s 964us/step - loss: 0.4698 - val_loss: 0.4661
Epoch 65/100
218/218 [==============================] - 0s 953us/step - loss: 0.4685 - val_loss: 0.4668
Epoch 66/100
218/218 [==============================] - 0s 962us/step - loss: 0.4694 - val_loss: 0.4640
Epoch 67/100
218/218 [==============================] - 0s 961us/step - loss: 0.4679 - val_loss: 0.4619
Epoch 68/100
218/218 [==============================] - 0s 959us/step - loss: 0.4669 - val_loss: 0.4635
Epoch 69/100
218/218 [==============================] - 0s 968us/step - loss: 0.4673 - val_loss: 0.4623
Epoch 70/100
218/218 [==============================] - 0s 959us/step - loss: 0.4657 - val_loss: 0.4635
Epoch 71/100
218/218 [==============================] - 0s 964us/step - loss: 0.4676 - val_loss: 0.4649
Epoch 72/100
218/218 [==============================] - 0s 952us/step - loss: 0.4668 - val_loss: 0.4634
Epoch 73/100
218/218 [==============================] - 0s 957us/step - loss: 0.4644 - val_loss: 0.4641
Epoch 74/100
218/218 [==============================] - 0s 969us/step - loss: 0.4652 - val_loss: 0.4618
Epoch 75/100
218/218 [==============================] - 0s 956us/step - loss: 0.4639 - val_loss: 0.4598
Epoch 76/100
218/218 [==============================] - 0s 956us/step - loss: 0.4638 - val_loss: 0.4618
Epoch 77/100
218/218 [==============================] - 0s 951us/step - loss: 0.4641 - val_loss: 0.4619
Epoch 78/100
218/218 [==============================] - 0s 954us/step - loss: 0.4646 - val_loss: 0.4614
Epoch 79/100
218/218 [==============================] - 0s 953us/step - loss: 0.4648 - val_loss: 0.4629
Epoch 80/100
218/218 [==============================] - 0s 954us/step - loss: 0.4641 - val_loss: 0.4634
Epoch 81/100
218/218 [==============================] - 0s 953us/step - loss: 0.4643 - val_loss: 0.4594
Epoch 82/100
218/218 [==============================] - 0s 951us/step - loss: 0.4656 - val_loss: 0.4592
Epoch 83/100
218/218 [==============================] - 0s 963us/step - loss: 0.4631 - val_loss: 0.4588
Epoch 84/100
218/218 [==============================] - 0s 966us/step - loss: 0.4647 - val_loss: 0.4632
Epoch 85/100
218/218 [==============================] - 0s 955us/step - loss: 0.4632 - val_loss: 0.4620
Epoch 86/100
218/218 [==============================] - 0s 955us/step - loss: 0.4626 - val_loss: 0.4573
Epoch 87/100
218/218 [==============================] - 0s 951us/step - loss: 0.4627 - val_loss: 0.4596
Epoch 88/100
218/218 [==============================] - 0s 960us/step - loss: 0.4637 - val_loss: 0.4606
Epoch 89/100
218/218 [==============================] - 0s 952us/step - loss: 0.4617 - val_loss: 0.4596
Epoch 90/100
218/218 [==============================] - 0s 955us/step - loss: 0.4625 - val_loss: 0.4631
Epoch 91/100
218/218 [==============================] - 0s 947us/step - loss: 0.4620 - val_loss: 0.4606
Epoch 92/100
218/218 [==============================] - 0s 952us/step - loss: 0.4644 - val_loss: 0.4615
Epoch 93/100
218/218 [==============================] - 0s 961us/step - loss: 0.4627 - val_loss: 0.4595
Epoch 94/100
218/218 [==============================] - 0s 953us/step - loss: 0.4621 - val_loss: 0.4593
Epoch 95/100
218/218 [==============================] - 0s 949us/step - loss: 0.4616 - val_loss: 0.4620
Epoch 96/100
218/218 [==============================] - 0s 955us/step - loss: 0.4624 - val_loss: 0.4568
Epoch 97/100
218/218 [==============================] - 0s 956us/step - loss: 0.4623 - val_loss: 0.4590
Epoch 98/100
218/218 [==============================] - 0s 949us/step - loss: 0.4601 - val_loss: 0.4581
Epoch 99/100
218/218 [==============================] - 0s 961us/step - loss: 0.4617 - val_loss: 0.4576
Epoch 100/100
218/218 [==============================] - 0s 949us/step - loss: 0.4616 - val_loss: 0.4549
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.07 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.34 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.86 seconds.
Calculating KMeans...
Calculated KMeans in 20.34 seconds.
Calculated landmark operator in 22.66 seconds.
Calculating optimal t...
Automatically selected t = 49
Calculated optimal t in 2.98 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.61 seconds.
Calculating metric MDS...
Calculated metric MDS in 23.48 seconds.
Calculated PHATE in 63.09 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_311 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_312 (Dense) (None, 32) 2080 dense_311[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_312[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_312[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_313 (Dense) (None, 32) 128
_________________________________________________________________
dense_314 (Dense) (None, 64) 2112
_________________________________________________________________
dense_315 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_311 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_312 (Dense) (None, 32) 2080 dense_311[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_312[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_312[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1030 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1030 [()] 0 tf_op_layer_Shape_1030[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_103 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1030[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_103[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_309 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_103 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_309[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1031 (TensorF [(2,)] 0 tf_op_layer_Add_103[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1033 (TensorF [(2,)] 0 tf_op_layer_Add_103[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1032 (TensorF [(2,)] 0 tf_op_layer_Add_103[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1034 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1036 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1035 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1037 (TensorF [(2,)] 0 tf_op_layer_Add_103[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1039 (TensorF [(2,)] 0 tf_op_layer_Add_103[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1038 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1031 [()] 0 tf_op_layer_Shape_1031[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1033 [()] 0 tf_op_layer_Shape_1033[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1032 [()] 0 tf_op_layer_Shape_1032[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1034 [()] 0 tf_op_layer_Shape_1034[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1036 [()] 0 tf_op_layer_Shape_1036[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1035 [()] 0 tf_op_layer_Shape_1035[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1037 [()] 0 tf_op_layer_Shape_1037[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1039 [()] 0 tf_op_layer_Shape_1039[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1038 [()] 0 tf_op_layer_Shape_1038[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_618/shape ( [(3,)] 0 tf_op_layer_strided_slice_1031[0]
tf_op_layer_strided_slice_1033[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_619/shape ( [(3,)] 0 tf_op_layer_strided_slice_1032[0]
tf_op_layer_strided_slice_1033[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_620/shape ( [(3,)] 0 tf_op_layer_strided_slice_1034[0]
tf_op_layer_strided_slice_1036[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_621/shape ( [(3,)] 0 tf_op_layer_strided_slice_1035[0]
tf_op_layer_strided_slice_1036[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_622/shape ( [(3,)] 0 tf_op_layer_strided_slice_1037[0]
tf_op_layer_strided_slice_1039[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_623/shape ( [(3,)] 0 tf_op_layer_strided_slice_1038[0]
tf_op_layer_strided_slice_1039[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_618 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_103[0][0]
tf_op_layer_Reshape_618/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_618/multiples [(3,)] 0 tf_op_layer_strided_slice_1032[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_619 (Tensor [(1, None, None)] 0 tf_op_layer_Add_103[0][0]
tf_op_layer_Reshape_619/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_619/multiples [(3,)] 0 tf_op_layer_strided_slice_1031[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_620 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_620/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_620/multiples [(3,)] 0 tf_op_layer_strided_slice_1035[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_621 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_621/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_621/multiples [(3,)] 0 tf_op_layer_strided_slice_1034[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_622 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_103[0][0]
tf_op_layer_Reshape_622/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_622/multiples [(3,)] 0 tf_op_layer_strided_slice_1038[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_623 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_623/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_623/multiples [(3,)] 0 tf_op_layer_strided_slice_1037[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_618 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_618[0][0]
tf_op_layer_Tile_618/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_619 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_619[0][0]
tf_op_layer_Tile_619/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_620 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_620[0][0]
tf_op_layer_Tile_620/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_621 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_621[0][0]
tf_op_layer_Tile_621/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_622 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_622[0][0]
tf_op_layer_Tile_622/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_623 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_623[0][0]
tf_op_layer_Tile_623/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_515 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_618[0][0]
tf_op_layer_Tile_619[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_516 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_620[0][0]
tf_op_layer_Tile_621[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_517 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_622[0][0]
tf_op_layer_Tile_623[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_412 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_515[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_413 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_516[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_414 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_517[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_721 (TensorFlo [(None, None)] 0 tf_op_layer_Square_412[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_722 (TensorFlo [(None, None)] 0 tf_op_layer_Square_413[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_723 (TensorFlo [(None, None)] 0 tf_op_layer_Square_414[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_309 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_721[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_309 (TensorFlo [()] 0 tf_op_layer_strided_slice_1033[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_310 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_722[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_310 (TensorFlo [()] 0 tf_op_layer_strided_slice_1036[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_311 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_723[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_311 (TensorFlo [()] 0 tf_op_layer_strided_slice_1039[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_309 (Tensor [(None, None)] 0 tf_op_layer_Neg_309[0][0]
tf_op_layer_Cast_309[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_310 (Tensor [(None, None)] 0 tf_op_layer_Neg_310[0][0]
tf_op_layer_Cast_310[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_311 (Tensor [(None, None)] 0 tf_op_layer_Neg_311[0][0]
tf_op_layer_Cast_311[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_309 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_309[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_310 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_310[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_311 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_311[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_724 (TensorFlo [()] 0 tf_op_layer_Exp_309[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_725 (TensorFlo [()] 0 tf_op_layer_Exp_310[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_726 (TensorFlo [()] 0 tf_op_layer_Exp_311[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_519 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_206 (TensorFl [()] 0 tf_op_layer_Mean_724[0][0]
tf_op_layer_Mean_725[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_310 (TensorFlow [()] 0 tf_op_layer_Mean_726[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_415 (TensorF [(None, 97)] 0 tf_op_layer_Sub_519[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_518 (TensorFlow [()] 0 tf_op_layer_AddV2_206[0][0]
tf_op_layer_Mul_310[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_103 (TensorFlow [(None,)] 0 tf_op_layer_Square_415[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_311 (TensorFlow [()] 0 tf_op_layer_Sub_518[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_207 (TensorFl [(None,)] 0 tf_op_layer_Sum_103[0][0]
tf_op_layer_Mul_311[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_727 (TensorFlo [()] 0 tf_op_layer_AddV2_207[0][0]
__________________________________________________________________________________________________
add_loss_103 (AddLoss) () 0 tf_op_layer_Mean_727[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 13s - loss: 8.5256WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.1269s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9127 - val_loss: 0.5255
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4379 - val_loss: 0.3869
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3901 - val_loss: 0.3790
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3832 - val_loss: 0.3743
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3777 - val_loss: 0.3709
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3741 - val_loss: 0.3701
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3738 - val_loss: 0.3649
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3699 - val_loss: 0.3626
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3678 - val_loss: 0.3639
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3663 - val_loss: 0.3606
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3656 - val_loss: 0.3610
Epoch 12/100
218/218 [==============================] - 0s 951us/step - loss: 0.3630 - val_loss: 0.3567
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3612 - val_loss: 0.3545
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3607 - val_loss: 0.3541
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3598 - val_loss: 0.3514
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3585 - val_loss: 0.3518
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3574 - val_loss: 0.3501
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3578 - val_loss: 0.3538
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3560 - val_loss: 0.3487
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3561 - val_loss: 0.3494
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3558 - val_loss: 0.3527
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3538 - val_loss: 0.3506
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3518
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3541 - val_loss: 0.3471
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3538 - val_loss: 0.3482
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3520 - val_loss: 0.3474
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3497
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3443
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3502 - val_loss: 0.3464
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3502
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3488
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3512 - val_loss: 0.3465
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3463
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3506 - val_loss: 0.3437
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3432
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3486 - val_loss: 0.3458
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3458
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3453
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3408
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3403
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3437
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3413
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3422
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3447
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3430
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3408
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3416
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3427
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3398
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3415
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3403
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3395
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3395
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3392
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3390
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3402
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3444
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3374
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3384
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3386
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3387
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3374
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3380
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3366
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3383
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3383
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3367
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3380
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3352
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3359
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3350
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3360
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3400 - val_loss: 0.3401
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3354
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3342
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3346
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3364
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3354
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3355
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3337
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3330
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3377
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3387 - val_loss: 0.3345
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3340
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3378 - val_loss: 0.3356
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3348
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3334
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3387 - val_loss: 0.3341
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3378 - val_loss: 0.3340
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3386 - val_loss: 0.3333
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3382 - val_loss: 0.3337
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3325
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3375 - val_loss: 0.3337
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3388 - val_loss: 0.3342
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3375 - val_loss: 0.3359
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3346
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3383 - val_loss: 0.3313
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3378 - val_loss: 0.3335
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3364 - val_loss: 0.3324
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3365 - val_loss: 0.3336
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_316 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1040 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1040 [()] 0 tf_op_layer_Shape_1040[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_104 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1040[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_104[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_312 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_104 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_312[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1041 (TensorF [(2,)] 0 tf_op_layer_Add_104[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1043 (TensorF [(2,)] 0 tf_op_layer_Add_104[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1042 (TensorF [(2,)] 0 tf_op_layer_Add_104[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1044 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1046 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1045 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1047 (TensorF [(2,)] 0 tf_op_layer_Add_104[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1049 (TensorF [(2,)] 0 tf_op_layer_Add_104[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1048 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1041 [()] 0 tf_op_layer_Shape_1041[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1043 [()] 0 tf_op_layer_Shape_1043[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1042 [()] 0 tf_op_layer_Shape_1042[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1044 [()] 0 tf_op_layer_Shape_1044[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1046 [()] 0 tf_op_layer_Shape_1046[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1045 [()] 0 tf_op_layer_Shape_1045[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1047 [()] 0 tf_op_layer_Shape_1047[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1049 [()] 0 tf_op_layer_Shape_1049[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1048 [()] 0 tf_op_layer_Shape_1048[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_624/shape ( [(3,)] 0 tf_op_layer_strided_slice_1041[0]
tf_op_layer_strided_slice_1043[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_625/shape ( [(3,)] 0 tf_op_layer_strided_slice_1042[0]
tf_op_layer_strided_slice_1043[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_626/shape ( [(3,)] 0 tf_op_layer_strided_slice_1044[0]
tf_op_layer_strided_slice_1046[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_627/shape ( [(3,)] 0 tf_op_layer_strided_slice_1045[0]
tf_op_layer_strided_slice_1046[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_628/shape ( [(3,)] 0 tf_op_layer_strided_slice_1047[0]
tf_op_layer_strided_slice_1049[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_629/shape ( [(3,)] 0 tf_op_layer_strided_slice_1048[0]
tf_op_layer_strided_slice_1049[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_624 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_104[0][0]
tf_op_layer_Reshape_624/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_624/multiples [(3,)] 0 tf_op_layer_strided_slice_1042[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_625 (Tensor [(1, None, None)] 0 tf_op_layer_Add_104[0][0]
tf_op_layer_Reshape_625/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_625/multiples [(3,)] 0 tf_op_layer_strided_slice_1041[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_626 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_626/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_626/multiples [(3,)] 0 tf_op_layer_strided_slice_1045[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_627 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_627/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_627/multiples [(3,)] 0 tf_op_layer_strided_slice_1044[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_628 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_104[0][0]
tf_op_layer_Reshape_628/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_628/multiples [(3,)] 0 tf_op_layer_strided_slice_1048[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_629 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_629/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_629/multiples [(3,)] 0 tf_op_layer_strided_slice_1047[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_624 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_624[0][0]
tf_op_layer_Tile_624/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_625 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_625[0][0]
tf_op_layer_Tile_625/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_626 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_626[0][0]
tf_op_layer_Tile_626/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_627 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_627[0][0]
tf_op_layer_Tile_627/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_628 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_628[0][0]
tf_op_layer_Tile_628/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_629 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_629[0][0]
tf_op_layer_Tile_629/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_520 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_624[0][0]
tf_op_layer_Tile_625[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_521 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_626[0][0]
tf_op_layer_Tile_627[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_522 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_628[0][0]
tf_op_layer_Tile_629[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_416 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_520[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_417 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_521[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_418 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_522[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_728 (TensorFlo [(None, None)] 0 tf_op_layer_Square_416[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_729 (TensorFlo [(None, None)] 0 tf_op_layer_Square_417[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_730 (TensorFlo [(None, None)] 0 tf_op_layer_Square_418[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_312 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_728[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_312 (TensorFlo [()] 0 tf_op_layer_strided_slice_1043[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_313 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_729[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_313 (TensorFlo [()] 0 tf_op_layer_strided_slice_1046[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_314 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_730[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_314 (TensorFlo [()] 0 tf_op_layer_strided_slice_1049[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_312 (Tensor [(None, None)] 0 tf_op_layer_Neg_312[0][0]
tf_op_layer_Cast_312[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_313 (Tensor [(None, None)] 0 tf_op_layer_Neg_313[0][0]
tf_op_layer_Cast_313[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_314 (Tensor [(None, None)] 0 tf_op_layer_Neg_314[0][0]
tf_op_layer_Cast_314[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_312 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_312[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_313 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_313[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_314 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_314[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_731 (TensorFlo [()] 0 tf_op_layer_Exp_312[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_732 (TensorFlo [()] 0 tf_op_layer_Exp_313[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_733 (TensorFlo [()] 0 tf_op_layer_Exp_314[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_524 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_208 (TensorFl [()] 0 tf_op_layer_Mean_731[0][0]
tf_op_layer_Mean_732[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_313 (TensorFlow [()] 0 tf_op_layer_Mean_733[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_419 (TensorF [(None, 97)] 0 tf_op_layer_Sub_524[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_523 (TensorFlow [()] 0 tf_op_layer_AddV2_208[0][0]
tf_op_layer_Mul_313[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_104 (TensorFlow [(None,)] 0 tf_op_layer_Square_419[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_314 (TensorFlow [()] 0 tf_op_layer_Sub_523[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_209 (TensorFl [(None,)] 0 tf_op_layer_Sum_104[0][0]
tf_op_layer_Mul_314[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_734 (TensorFlo [()] 0 tf_op_layer_AddV2_209[0][0]
__________________________________________________________________________________________________
add_loss_104 (AddLoss) () 0 tf_op_layer_Mean_734[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 6.9923WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0977s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 2.0283 - val_loss: 1.0440
Epoch 2/100
218/218 [==============================] - 0s 960us/step - loss: 0.8954 - val_loss: 0.8279
Epoch 3/100
218/218 [==============================] - 0s 963us/step - loss: 0.7673 - val_loss: 0.7354
Epoch 4/100
218/218 [==============================] - 0s 957us/step - loss: 0.7080 - val_loss: 0.6851
Epoch 5/100
218/218 [==============================] - 0s 963us/step - loss: 0.6674 - val_loss: 0.6484
Epoch 6/100
218/218 [==============================] - 0s 960us/step - loss: 0.6290 - val_loss: 0.6093
Epoch 7/100
218/218 [==============================] - 0s 960us/step - loss: 0.6024 - val_loss: 0.5881
Epoch 8/100
218/218 [==============================] - 0s 962us/step - loss: 0.5822 - val_loss: 0.5694
Epoch 9/100
218/218 [==============================] - 0s 960us/step - loss: 0.5629 - val_loss: 0.5581
Epoch 10/100
218/218 [==============================] - 0s 966us/step - loss: 0.5549 - val_loss: 0.5519
Epoch 11/100
218/218 [==============================] - 0s 960us/step - loss: 0.5459 - val_loss: 0.5381
Epoch 12/100
218/218 [==============================] - 0s 965us/step - loss: 0.5364 - val_loss: 0.5359
Epoch 13/100
218/218 [==============================] - 0s 956us/step - loss: 0.5330 - val_loss: 0.5310
Epoch 14/100
218/218 [==============================] - 0s 957us/step - loss: 0.5295 - val_loss: 0.5247
Epoch 15/100
218/218 [==============================] - 0s 968us/step - loss: 0.5244 - val_loss: 0.5180
Epoch 16/100
218/218 [==============================] - 0s 956us/step - loss: 0.5210 - val_loss: 0.5201
Epoch 17/100
218/218 [==============================] - 0s 959us/step - loss: 0.5144 - val_loss: 0.5139
Epoch 18/100
218/218 [==============================] - 0s 955us/step - loss: 0.5111 - val_loss: 0.5095
Epoch 19/100
218/218 [==============================] - 0s 958us/step - loss: 0.5089 - val_loss: 0.5062
Epoch 20/100
218/218 [==============================] - 0s 962us/step - loss: 0.5065 - val_loss: 0.5030
Epoch 21/100
218/218 [==============================] - 0s 964us/step - loss: 0.5054 - val_loss: 0.5029
Epoch 22/100
218/218 [==============================] - 0s 972us/step - loss: 0.5014 - val_loss: 0.4943
Epoch 23/100
218/218 [==============================] - 0s 959us/step - loss: 0.4988 - val_loss: 0.4960
Epoch 24/100
218/218 [==============================] - 0s 961us/step - loss: 0.4989 - val_loss: 0.4965
Epoch 25/100
218/218 [==============================] - 0s 961us/step - loss: 0.4952 - val_loss: 0.4893
Epoch 26/100
218/218 [==============================] - 0s 961us/step - loss: 0.4961 - val_loss: 0.4857
Epoch 27/100
218/218 [==============================] - 0s 969us/step - loss: 0.4933 - val_loss: 0.4907
Epoch 28/100
218/218 [==============================] - 0s 993us/step - loss: 0.4890 - val_loss: 0.4853
Epoch 29/100
218/218 [==============================] - 0s 981us/step - loss: 0.4888 - val_loss: 0.4867
Epoch 30/100
218/218 [==============================] - 0s 980us/step - loss: 0.4914 - val_loss: 0.4862
Epoch 31/100
218/218 [==============================] - 0s 981us/step - loss: 0.4887 - val_loss: 0.4870
Epoch 32/100
218/218 [==============================] - 0s 969us/step - loss: 0.4881 - val_loss: 0.4800
Epoch 33/100
218/218 [==============================] - 0s 969us/step - loss: 0.4863 - val_loss: 0.4815
Epoch 34/100
218/218 [==============================] - 0s 971us/step - loss: 0.4822 - val_loss: 0.4771
Epoch 35/100
218/218 [==============================] - 0s 967us/step - loss: 0.4848 - val_loss: 0.4862
Epoch 36/100
218/218 [==============================] - 0s 959us/step - loss: 0.4827 - val_loss: 0.4796
Epoch 37/100
218/218 [==============================] - 0s 959us/step - loss: 0.4841 - val_loss: 0.4845
Epoch 38/100
218/218 [==============================] - 0s 965us/step - loss: 0.4829 - val_loss: 0.4805
Epoch 39/100
218/218 [==============================] - 0s 965us/step - loss: 0.4819 - val_loss: 0.4808
Epoch 40/100
218/218 [==============================] - 0s 965us/step - loss: 0.4803 - val_loss: 0.4861
Epoch 41/100
218/218 [==============================] - 0s 965us/step - loss: 0.4787 - val_loss: 0.4731
Epoch 42/100
218/218 [==============================] - 0s 964us/step - loss: 0.4810 - val_loss: 0.4739
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4782 - val_loss: 0.4770
Epoch 44/100
218/218 [==============================] - 0s 997us/step - loss: 0.4771 - val_loss: 0.4782
Epoch 45/100
218/218 [==============================] - 0s 990us/step - loss: 0.4774 - val_loss: 0.4737
Epoch 46/100
218/218 [==============================] - 0s 972us/step - loss: 0.4775 - val_loss: 0.4712
Epoch 47/100
218/218 [==============================] - 0s 969us/step - loss: 0.4769 - val_loss: 0.4740
Epoch 48/100
218/218 [==============================] - 0s 973us/step - loss: 0.4788 - val_loss: 0.4744
Epoch 49/100
218/218 [==============================] - 0s 991us/step - loss: 0.4727 - val_loss: 0.4696
Epoch 50/100
218/218 [==============================] - 0s 980us/step - loss: 0.4750 - val_loss: 0.4737
Epoch 51/100
218/218 [==============================] - 0s 971us/step - loss: 0.4745 - val_loss: 0.4700
Epoch 52/100
218/218 [==============================] - 0s 977us/step - loss: 0.4731 - val_loss: 0.4697
Epoch 53/100
218/218 [==============================] - 0s 969us/step - loss: 0.4747 - val_loss: 0.4783
Epoch 54/100
218/218 [==============================] - 0s 967us/step - loss: 0.4747 - val_loss: 0.4695
Epoch 55/100
218/218 [==============================] - 0s 973us/step - loss: 0.4720 - val_loss: 0.4677
Epoch 56/100
218/218 [==============================] - 0s 983us/step - loss: 0.4719 - val_loss: 0.4694
Epoch 57/100
218/218 [==============================] - 0s 987us/step - loss: 0.4732 - val_loss: 0.4669
Epoch 58/100
218/218 [==============================] - 0s 968us/step - loss: 0.4712 - val_loss: 0.4663
Epoch 59/100
218/218 [==============================] - 0s 965us/step - loss: 0.4706 - val_loss: 0.4683
Epoch 60/100
218/218 [==============================] - 0s 964us/step - loss: 0.4714 - val_loss: 0.4659
Epoch 61/100
218/218 [==============================] - 0s 966us/step - loss: 0.4700 - val_loss: 0.4669
Epoch 62/100
218/218 [==============================] - 0s 964us/step - loss: 0.4701 - val_loss: 0.4677
Epoch 63/100
218/218 [==============================] - 0s 963us/step - loss: 0.4703 - val_loss: 0.4672
Epoch 64/100
218/218 [==============================] - 0s 961us/step - loss: 0.4698 - val_loss: 0.4683
Epoch 65/100
218/218 [==============================] - 0s 961us/step - loss: 0.4706 - val_loss: 0.4680
Epoch 66/100
218/218 [==============================] - 0s 969us/step - loss: 0.4685 - val_loss: 0.4649
Epoch 67/100
218/218 [==============================] - 0s 963us/step - loss: 0.4700 - val_loss: 0.4676
Epoch 68/100
218/218 [==============================] - 0s 963us/step - loss: 0.4691 - val_loss: 0.4654
Epoch 69/100
218/218 [==============================] - 0s 955us/step - loss: 0.4678 - val_loss: 0.4631
Epoch 70/100
218/218 [==============================] - 0s 965us/step - loss: 0.4673 - val_loss: 0.4636
Epoch 71/100
218/218 [==============================] - 0s 968us/step - loss: 0.4666 - val_loss: 0.4598
Epoch 72/100
218/218 [==============================] - 0s 966us/step - loss: 0.4692 - val_loss: 0.4626
Epoch 73/100
218/218 [==============================] - 0s 968us/step - loss: 0.4676 - val_loss: 0.4687
Epoch 74/100
218/218 [==============================] - 0s 969us/step - loss: 0.4664 - val_loss: 0.4644
Epoch 75/100
218/218 [==============================] - 0s 974us/step - loss: 0.4660 - val_loss: 0.4635
Epoch 76/100
218/218 [==============================] - 0s 965us/step - loss: 0.4663 - val_loss: 0.4620
Epoch 77/100
218/218 [==============================] - 0s 957us/step - loss: 0.4669 - val_loss: 0.4603
Epoch 78/100
218/218 [==============================] - 0s 959us/step - loss: 0.4680 - val_loss: 0.4646
Epoch 79/100
218/218 [==============================] - 0s 964us/step - loss: 0.4662 - val_loss: 0.4612
Epoch 80/100
218/218 [==============================] - 0s 968us/step - loss: 0.4661 - val_loss: 0.4628
Epoch 81/100
218/218 [==============================] - 0s 963us/step - loss: 0.4666 - val_loss: 0.4625
Epoch 82/100
218/218 [==============================] - 0s 962us/step - loss: 0.4647 - val_loss: 0.4621
Epoch 83/100
218/218 [==============================] - 0s 961us/step - loss: 0.4656 - val_loss: 0.4580
Epoch 84/100
218/218 [==============================] - 0s 961us/step - loss: 0.4649 - val_loss: 0.4646
Epoch 85/100
218/218 [==============================] - 0s 973us/step - loss: 0.4659 - val_loss: 0.4625
Epoch 86/100
218/218 [==============================] - 0s 965us/step - loss: 0.4650 - val_loss: 0.4556
Epoch 87/100
218/218 [==============================] - 0s 970us/step - loss: 0.4669 - val_loss: 0.4636
Epoch 88/100
218/218 [==============================] - 0s 976us/step - loss: 0.4646 - val_loss: 0.4602
Epoch 89/100
218/218 [==============================] - 0s 969us/step - loss: 0.4659 - val_loss: 0.4583
Epoch 90/100
218/218 [==============================] - 0s 966us/step - loss: 0.4645 - val_loss: 0.4637
Epoch 91/100
218/218 [==============================] - 0s 961us/step - loss: 0.4648 - val_loss: 0.4656
Epoch 92/100
218/218 [==============================] - 0s 968us/step - loss: 0.4647 - val_loss: 0.4627
Epoch 93/100
218/218 [==============================] - 0s 964us/step - loss: 0.4640 - val_loss: 0.4626
Epoch 94/100
218/218 [==============================] - 0s 966us/step - loss: 0.4638 - val_loss: 0.4606
Epoch 95/100
218/218 [==============================] - 0s 963us/step - loss: 0.4625 - val_loss: 0.4597
Epoch 96/100
218/218 [==============================] - 0s 964us/step - loss: 0.4656 - val_loss: 0.4588
Epoch 97/100
218/218 [==============================] - 0s 963us/step - loss: 0.4624 - val_loss: 0.4617
Epoch 98/100
218/218 [==============================] - 0s 973us/step - loss: 0.4634 - val_loss: 0.4550
Epoch 99/100
218/218 [==============================] - 0s 969us/step - loss: 0.4615 - val_loss: 0.4606
Epoch 100/100
218/218 [==============================] - 0s 965us/step - loss: 0.4653 - val_loss: 0.4610
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.11 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.38 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.86 seconds.
Calculating KMeans...
Calculated KMeans in 25.10 seconds.
Calculated landmark operator in 27.40 seconds.
Calculating optimal t...
Automatically selected t = 29
Calculated optimal t in 2.90 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.62 seconds.
Calculating metric MDS...
Calculated metric MDS in 17.03 seconds.
Calculated PHATE in 61.34 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_317 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_318 (Dense) (None, 32) 2080 dense_317[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_318[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_318[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_319 (Dense) (None, 32) 128
_________________________________________________________________
dense_320 (Dense) (None, 64) 2112
_________________________________________________________________
dense_321 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_317 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_318 (Dense) (None, 32) 2080 dense_317[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_318[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_318[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1050 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1050 [()] 0 tf_op_layer_Shape_1050[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_105 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1050[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_105[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_315 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_105 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_315[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1051 (TensorF [(2,)] 0 tf_op_layer_Add_105[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1053 (TensorF [(2,)] 0 tf_op_layer_Add_105[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1052 (TensorF [(2,)] 0 tf_op_layer_Add_105[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1054 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1056 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1055 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1057 (TensorF [(2,)] 0 tf_op_layer_Add_105[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1059 (TensorF [(2,)] 0 tf_op_layer_Add_105[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1058 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1051 [()] 0 tf_op_layer_Shape_1051[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1053 [()] 0 tf_op_layer_Shape_1053[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1052 [()] 0 tf_op_layer_Shape_1052[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1054 [()] 0 tf_op_layer_Shape_1054[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1056 [()] 0 tf_op_layer_Shape_1056[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1055 [()] 0 tf_op_layer_Shape_1055[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1057 [()] 0 tf_op_layer_Shape_1057[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1059 [()] 0 tf_op_layer_Shape_1059[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1058 [()] 0 tf_op_layer_Shape_1058[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_630/shape ( [(3,)] 0 tf_op_layer_strided_slice_1051[0]
tf_op_layer_strided_slice_1053[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_631/shape ( [(3,)] 0 tf_op_layer_strided_slice_1052[0]
tf_op_layer_strided_slice_1053[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_632/shape ( [(3,)] 0 tf_op_layer_strided_slice_1054[0]
tf_op_layer_strided_slice_1056[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_633/shape ( [(3,)] 0 tf_op_layer_strided_slice_1055[0]
tf_op_layer_strided_slice_1056[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_634/shape ( [(3,)] 0 tf_op_layer_strided_slice_1057[0]
tf_op_layer_strided_slice_1059[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_635/shape ( [(3,)] 0 tf_op_layer_strided_slice_1058[0]
tf_op_layer_strided_slice_1059[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_630 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_105[0][0]
tf_op_layer_Reshape_630/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_630/multiples [(3,)] 0 tf_op_layer_strided_slice_1052[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_631 (Tensor [(1, None, None)] 0 tf_op_layer_Add_105[0][0]
tf_op_layer_Reshape_631/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_631/multiples [(3,)] 0 tf_op_layer_strided_slice_1051[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_632 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_632/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_632/multiples [(3,)] 0 tf_op_layer_strided_slice_1055[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_633 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_633/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_633/multiples [(3,)] 0 tf_op_layer_strided_slice_1054[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_634 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_105[0][0]
tf_op_layer_Reshape_634/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_634/multiples [(3,)] 0 tf_op_layer_strided_slice_1058[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_635 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_635/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_635/multiples [(3,)] 0 tf_op_layer_strided_slice_1057[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_630 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_630[0][0]
tf_op_layer_Tile_630/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_631 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_631[0][0]
tf_op_layer_Tile_631/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_632 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_632[0][0]
tf_op_layer_Tile_632/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_633 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_633[0][0]
tf_op_layer_Tile_633/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_634 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_634[0][0]
tf_op_layer_Tile_634/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_635 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_635[0][0]
tf_op_layer_Tile_635/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_525 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_630[0][0]
tf_op_layer_Tile_631[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_526 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_632[0][0]
tf_op_layer_Tile_633[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_527 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_634[0][0]
tf_op_layer_Tile_635[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_420 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_525[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_421 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_526[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_422 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_527[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_735 (TensorFlo [(None, None)] 0 tf_op_layer_Square_420[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_736 (TensorFlo [(None, None)] 0 tf_op_layer_Square_421[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_737 (TensorFlo [(None, None)] 0 tf_op_layer_Square_422[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_315 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_735[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_315 (TensorFlo [()] 0 tf_op_layer_strided_slice_1053[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_316 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_736[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_316 (TensorFlo [()] 0 tf_op_layer_strided_slice_1056[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_317 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_737[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_317 (TensorFlo [()] 0 tf_op_layer_strided_slice_1059[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_315 (Tensor [(None, None)] 0 tf_op_layer_Neg_315[0][0]
tf_op_layer_Cast_315[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_316 (Tensor [(None, None)] 0 tf_op_layer_Neg_316[0][0]
tf_op_layer_Cast_316[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_317 (Tensor [(None, None)] 0 tf_op_layer_Neg_317[0][0]
tf_op_layer_Cast_317[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_315 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_315[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_316 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_316[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_317 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_317[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_738 (TensorFlo [()] 0 tf_op_layer_Exp_315[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_739 (TensorFlo [()] 0 tf_op_layer_Exp_316[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_740 (TensorFlo [()] 0 tf_op_layer_Exp_317[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_529 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_210 (TensorFl [()] 0 tf_op_layer_Mean_738[0][0]
tf_op_layer_Mean_739[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_316 (TensorFlow [()] 0 tf_op_layer_Mean_740[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_423 (TensorF [(None, 97)] 0 tf_op_layer_Sub_529[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_528 (TensorFlow [()] 0 tf_op_layer_AddV2_210[0][0]
tf_op_layer_Mul_316[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_105 (TensorFlow [(None,)] 0 tf_op_layer_Square_423[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_317 (TensorFlow [()] 0 tf_op_layer_Sub_528[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_211 (TensorFl [(None,)] 0 tf_op_layer_Sum_105[0][0]
tf_op_layer_Mul_317[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_741 (TensorFlo [()] 0 tf_op_layer_AddV2_211[0][0]
__________________________________________________________________________________________________
add_loss_105 (AddLoss) () 0 tf_op_layer_Mean_741[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 7.7459WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.1109s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9279 - val_loss: 0.4939
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4801 - val_loss: 0.4077
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4057 - val_loss: 0.3752
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3897 - val_loss: 0.3642
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3827 - val_loss: 0.3573
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3802 - val_loss: 0.3594
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3765 - val_loss: 0.3545
Epoch 8/100
218/218 [==============================] - 0s 954us/step - loss: 0.3712 - val_loss: 0.3514
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3700 - val_loss: 0.3505
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3677 - val_loss: 0.3462
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3659 - val_loss: 0.3445
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3640 - val_loss: 0.3431
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3624 - val_loss: 0.3416
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3613 - val_loss: 0.3429
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3606 - val_loss: 0.3387
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3587 - val_loss: 0.3374
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3577 - val_loss: 0.3384
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3589 - val_loss: 0.3396
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3565 - val_loss: 0.3345
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3570 - val_loss: 0.3343
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3570 - val_loss: 0.3345
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3549 - val_loss: 0.3331
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3552 - val_loss: 0.3347
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3548 - val_loss: 0.3344
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3550 - val_loss: 0.3340
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3546 - val_loss: 0.3332
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3537 - val_loss: 0.3322
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3532 - val_loss: 0.3329
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3528 - val_loss: 0.3338
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3532 - val_loss: 0.3301
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3516 - val_loss: 0.3299
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3318
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3517 - val_loss: 0.3315
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3508 - val_loss: 0.3284
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3507 - val_loss: 0.3280
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3258
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3293
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3272
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3497 - val_loss: 0.3325
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3293
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3271
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3272
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3496 - val_loss: 0.3263
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3280
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3270
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3228
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3274
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3259
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3279
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3253
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3257
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3264
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3259
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3242
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3244
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3261
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3455 - val_loss: 0.3255
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3226
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3237
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3221
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3204
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3248
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3197
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3218
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3217
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3190
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3221
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3203
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3211
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3214
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3209
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3228
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3192
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3217
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3206
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3204
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3220
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3186
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3220
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3192
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3237
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3219
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3190
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3200
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3192
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3203
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3180
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3210
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3405 - val_loss: 0.3193
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3184
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3217
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3214
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3197
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3187
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3207
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3220
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3191
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3235
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3198
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3197
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_322 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1060 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1060 [()] 0 tf_op_layer_Shape_1060[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_106 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1060[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_106[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_318 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_106 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_318[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1061 (TensorF [(2,)] 0 tf_op_layer_Add_106[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1063 (TensorF [(2,)] 0 tf_op_layer_Add_106[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1062 (TensorF [(2,)] 0 tf_op_layer_Add_106[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1064 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1066 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1065 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1067 (TensorF [(2,)] 0 tf_op_layer_Add_106[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1069 (TensorF [(2,)] 0 tf_op_layer_Add_106[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1068 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1061 [()] 0 tf_op_layer_Shape_1061[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1063 [()] 0 tf_op_layer_Shape_1063[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1062 [()] 0 tf_op_layer_Shape_1062[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1064 [()] 0 tf_op_layer_Shape_1064[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1066 [()] 0 tf_op_layer_Shape_1066[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1065 [()] 0 tf_op_layer_Shape_1065[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1067 [()] 0 tf_op_layer_Shape_1067[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1069 [()] 0 tf_op_layer_Shape_1069[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1068 [()] 0 tf_op_layer_Shape_1068[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_636/shape ( [(3,)] 0 tf_op_layer_strided_slice_1061[0]
tf_op_layer_strided_slice_1063[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_637/shape ( [(3,)] 0 tf_op_layer_strided_slice_1062[0]
tf_op_layer_strided_slice_1063[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_638/shape ( [(3,)] 0 tf_op_layer_strided_slice_1064[0]
tf_op_layer_strided_slice_1066[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_639/shape ( [(3,)] 0 tf_op_layer_strided_slice_1065[0]
tf_op_layer_strided_slice_1066[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_640/shape ( [(3,)] 0 tf_op_layer_strided_slice_1067[0]
tf_op_layer_strided_slice_1069[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_641/shape ( [(3,)] 0 tf_op_layer_strided_slice_1068[0]
tf_op_layer_strided_slice_1069[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_636 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_106[0][0]
tf_op_layer_Reshape_636/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_636/multiples [(3,)] 0 tf_op_layer_strided_slice_1062[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_637 (Tensor [(1, None, None)] 0 tf_op_layer_Add_106[0][0]
tf_op_layer_Reshape_637/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_637/multiples [(3,)] 0 tf_op_layer_strided_slice_1061[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_638 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_638/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_638/multiples [(3,)] 0 tf_op_layer_strided_slice_1065[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_639 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_639/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_639/multiples [(3,)] 0 tf_op_layer_strided_slice_1064[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_640 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_106[0][0]
tf_op_layer_Reshape_640/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_640/multiples [(3,)] 0 tf_op_layer_strided_slice_1068[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_641 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_641/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_641/multiples [(3,)] 0 tf_op_layer_strided_slice_1067[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_636 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_636[0][0]
tf_op_layer_Tile_636/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_637 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_637[0][0]
tf_op_layer_Tile_637/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_638 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_638[0][0]
tf_op_layer_Tile_638/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_639 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_639[0][0]
tf_op_layer_Tile_639/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_640 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_640[0][0]
tf_op_layer_Tile_640/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_641 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_641[0][0]
tf_op_layer_Tile_641/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_530 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_636[0][0]
tf_op_layer_Tile_637[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_531 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_638[0][0]
tf_op_layer_Tile_639[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_532 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_640[0][0]
tf_op_layer_Tile_641[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_424 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_530[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_425 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_531[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_426 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_532[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_742 (TensorFlo [(None, None)] 0 tf_op_layer_Square_424[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_743 (TensorFlo [(None, None)] 0 tf_op_layer_Square_425[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_744 (TensorFlo [(None, None)] 0 tf_op_layer_Square_426[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_318 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_742[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_318 (TensorFlo [()] 0 tf_op_layer_strided_slice_1063[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_319 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_743[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_319 (TensorFlo [()] 0 tf_op_layer_strided_slice_1066[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_320 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_744[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_320 (TensorFlo [()] 0 tf_op_layer_strided_slice_1069[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_318 (Tensor [(None, None)] 0 tf_op_layer_Neg_318[0][0]
tf_op_layer_Cast_318[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_319 (Tensor [(None, None)] 0 tf_op_layer_Neg_319[0][0]
tf_op_layer_Cast_319[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_320 (Tensor [(None, None)] 0 tf_op_layer_Neg_320[0][0]
tf_op_layer_Cast_320[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_318 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_318[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_319 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_319[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_320 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_320[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_745 (TensorFlo [()] 0 tf_op_layer_Exp_318[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_746 (TensorFlo [()] 0 tf_op_layer_Exp_319[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_747 (TensorFlo [()] 0 tf_op_layer_Exp_320[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_534 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_212 (TensorFl [()] 0 tf_op_layer_Mean_745[0][0]
tf_op_layer_Mean_746[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_319 (TensorFlow [()] 0 tf_op_layer_Mean_747[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_427 (TensorF [(None, 97)] 0 tf_op_layer_Sub_534[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_533 (TensorFlow [()] 0 tf_op_layer_AddV2_212[0][0]
tf_op_layer_Mul_319[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_106 (TensorFlow [(None,)] 0 tf_op_layer_Square_427[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_320 (TensorFlow [()] 0 tf_op_layer_Sub_533[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_213 (TensorFl [(None,)] 0 tf_op_layer_Sum_106[0][0]
tf_op_layer_Mul_320[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_748 (TensorFlo [()] 0 tf_op_layer_AddV2_213[0][0]
__________________________________________________________________________________________________
add_loss_106 (AddLoss) () 0 tf_op_layer_Mean_748[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 8.3429WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0966s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9828 - val_loss: 0.9742
Epoch 2/100
218/218 [==============================] - 0s 956us/step - loss: 0.8314 - val_loss: 0.7482
Epoch 3/100
218/218 [==============================] - 0s 943us/step - loss: 0.6881 - val_loss: 0.6635
Epoch 4/100
218/218 [==============================] - 0s 953us/step - loss: 0.6318 - val_loss: 0.6183
Epoch 5/100
218/218 [==============================] - 0s 944us/step - loss: 0.5929 - val_loss: 0.5902
Epoch 6/100
218/218 [==============================] - 0s 943us/step - loss: 0.5781 - val_loss: 0.5784
Epoch 7/100
218/218 [==============================] - 0s 915us/step - loss: 0.5633 - val_loss: 0.5682
Epoch 8/100
218/218 [==============================] - 0s 944us/step - loss: 0.5505 - val_loss: 0.5486
Epoch 9/100
218/218 [==============================] - 0s 940us/step - loss: 0.5424 - val_loss: 0.5473
Epoch 10/100
218/218 [==============================] - 0s 919us/step - loss: 0.5352 - val_loss: 0.5400
Epoch 11/100
218/218 [==============================] - 0s 928us/step - loss: 0.5299 - val_loss: 0.5293
Epoch 12/100
218/218 [==============================] - 0s 916us/step - loss: 0.5230 - val_loss: 0.5263
Epoch 13/100
218/218 [==============================] - 0s 914us/step - loss: 0.5192 - val_loss: 0.5281
Epoch 14/100
218/218 [==============================] - 0s 921us/step - loss: 0.5135 - val_loss: 0.5183
Epoch 15/100
218/218 [==============================] - 0s 917us/step - loss: 0.5103 - val_loss: 0.5174
Epoch 16/100
218/218 [==============================] - 0s 917us/step - loss: 0.5103 - val_loss: 0.5134
Epoch 17/100
218/218 [==============================] - 0s 914us/step - loss: 0.5060 - val_loss: 0.5084
Epoch 18/100
218/218 [==============================] - 0s 912us/step - loss: 0.5027 - val_loss: 0.5093
Epoch 19/100
218/218 [==============================] - 0s 915us/step - loss: 0.5007 - val_loss: 0.5074
Epoch 20/100
218/218 [==============================] - 0s 912us/step - loss: 0.4998 - val_loss: 0.5034
Epoch 21/100
218/218 [==============================] - 0s 919us/step - loss: 0.4952 - val_loss: 0.5083
Epoch 22/100
218/218 [==============================] - 0s 918us/step - loss: 0.4931 - val_loss: 0.5025
Epoch 23/100
218/218 [==============================] - 0s 914us/step - loss: 0.4916 - val_loss: 0.4983
Epoch 24/100
218/218 [==============================] - 0s 919us/step - loss: 0.4887 - val_loss: 0.5015
Epoch 25/100
218/218 [==============================] - 0s 916us/step - loss: 0.4882 - val_loss: 0.4931
Epoch 26/100
218/218 [==============================] - 0s 923us/step - loss: 0.4884 - val_loss: 0.4947
Epoch 27/100
218/218 [==============================] - 0s 918us/step - loss: 0.4871 - val_loss: 0.4940
Epoch 28/100
218/218 [==============================] - 0s 914us/step - loss: 0.4879 - val_loss: 0.4982
Epoch 29/100
218/218 [==============================] - 0s 914us/step - loss: 0.4849 - val_loss: 0.4944
Epoch 30/100
218/218 [==============================] - 0s 917us/step - loss: 0.4824 - val_loss: 0.4924
Epoch 31/100
218/218 [==============================] - 0s 921us/step - loss: 0.4845 - val_loss: 0.4927
Epoch 32/100
218/218 [==============================] - 0s 915us/step - loss: 0.4800 - val_loss: 0.4888
Epoch 33/100
218/218 [==============================] - 0s 933us/step - loss: 0.4809 - val_loss: 0.4922
Epoch 34/100
218/218 [==============================] - 0s 923us/step - loss: 0.4808 - val_loss: 0.4861
Epoch 35/100
218/218 [==============================] - 0s 920us/step - loss: 0.4793 - val_loss: 0.4881
Epoch 36/100
218/218 [==============================] - 0s 925us/step - loss: 0.4788 - val_loss: 0.4817
Epoch 37/100
218/218 [==============================] - 0s 919us/step - loss: 0.4753 - val_loss: 0.4861
Epoch 38/100
218/218 [==============================] - 0s 920us/step - loss: 0.4757 - val_loss: 0.4865
Epoch 39/100
218/218 [==============================] - 0s 922us/step - loss: 0.4738 - val_loss: 0.4864
Epoch 40/100
218/218 [==============================] - 0s 923us/step - loss: 0.4774 - val_loss: 0.4879
Epoch 41/100
218/218 [==============================] - 0s 932us/step - loss: 0.4725 - val_loss: 0.4846
Epoch 42/100
218/218 [==============================] - 0s 920us/step - loss: 0.4763 - val_loss: 0.4826
Epoch 43/100
218/218 [==============================] - 0s 927us/step - loss: 0.4746 - val_loss: 0.4802
Epoch 44/100
218/218 [==============================] - 0s 923us/step - loss: 0.4728 - val_loss: 0.4802
Epoch 45/100
218/218 [==============================] - 0s 932us/step - loss: 0.4740 - val_loss: 0.4869
Epoch 46/100
218/218 [==============================] - 0s 950us/step - loss: 0.4724 - val_loss: 0.4781
Epoch 47/100
218/218 [==============================] - 0s 921us/step - loss: 0.4715 - val_loss: 0.4807
Epoch 48/100
218/218 [==============================] - 0s 930us/step - loss: 0.4706 - val_loss: 0.4805
Epoch 49/100
218/218 [==============================] - 0s 895us/step - loss: 0.4696 - val_loss: 0.4779
Epoch 50/100
218/218 [==============================] - 0s 938us/step - loss: 0.4717 - val_loss: 0.4785
Epoch 51/100
218/218 [==============================] - 0s 936us/step - loss: 0.4730 - val_loss: 0.4785
Epoch 52/100
218/218 [==============================] - 0s 929us/step - loss: 0.4705 - val_loss: 0.4769
Epoch 53/100
218/218 [==============================] - 0s 923us/step - loss: 0.4689 - val_loss: 0.4772
Epoch 54/100
218/218 [==============================] - 0s 924us/step - loss: 0.4670 - val_loss: 0.4807
Epoch 55/100
218/218 [==============================] - 0s 920us/step - loss: 0.4702 - val_loss: 0.4797
Epoch 56/100
218/218 [==============================] - 0s 928us/step - loss: 0.4685 - val_loss: 0.4735
Epoch 57/100
218/218 [==============================] - 0s 917us/step - loss: 0.4671 - val_loss: 0.4767
Epoch 58/100
218/218 [==============================] - 0s 921us/step - loss: 0.4698 - val_loss: 0.4728
Epoch 59/100
218/218 [==============================] - 0s 919us/step - loss: 0.4664 - val_loss: 0.4784
Epoch 60/100
218/218 [==============================] - 0s 925us/step - loss: 0.4665 - val_loss: 0.4769
Epoch 61/100
218/218 [==============================] - 0s 930us/step - loss: 0.4682 - val_loss: 0.4750
Epoch 62/100
218/218 [==============================] - 0s 927us/step - loss: 0.4672 - val_loss: 0.4784
Epoch 63/100
218/218 [==============================] - 0s 934us/step - loss: 0.4664 - val_loss: 0.4785
Epoch 64/100
218/218 [==============================] - 0s 927us/step - loss: 0.4666 - val_loss: 0.4750
Epoch 65/100
218/218 [==============================] - 0s 930us/step - loss: 0.4638 - val_loss: 0.4770
Epoch 66/100
218/218 [==============================] - 0s 926us/step - loss: 0.4649 - val_loss: 0.4735
Epoch 67/100
218/218 [==============================] - 0s 935us/step - loss: 0.4656 - val_loss: 0.4732
Epoch 68/100
218/218 [==============================] - 0s 929us/step - loss: 0.4648 - val_loss: 0.4738
Epoch 69/100
218/218 [==============================] - 0s 925us/step - loss: 0.4647 - val_loss: 0.4730
Epoch 70/100
218/218 [==============================] - 0s 941us/step - loss: 0.4649 - val_loss: 0.4698
Epoch 71/100
218/218 [==============================] - 0s 948us/step - loss: 0.4655 - val_loss: 0.4774
Epoch 72/100
218/218 [==============================] - 0s 951us/step - loss: 0.4637 - val_loss: 0.4710
Epoch 73/100
218/218 [==============================] - 0s 966us/step - loss: 0.4644 - val_loss: 0.4722
Epoch 74/100
218/218 [==============================] - 0s 960us/step - loss: 0.4633 - val_loss: 0.4701
Epoch 75/100
218/218 [==============================] - 0s 955us/step - loss: 0.4604 - val_loss: 0.4725
Epoch 76/100
218/218 [==============================] - 0s 954us/step - loss: 0.4625 - val_loss: 0.4735
Epoch 77/100
218/218 [==============================] - 0s 972us/step - loss: 0.4648 - val_loss: 0.4777
Epoch 78/100
218/218 [==============================] - 0s 954us/step - loss: 0.4616 - val_loss: 0.4724
Epoch 79/100
218/218 [==============================] - 0s 955us/step - loss: 0.4632 - val_loss: 0.4694
Epoch 80/100
218/218 [==============================] - 0s 972us/step - loss: 0.4644 - val_loss: 0.4756
Epoch 81/100
218/218 [==============================] - 0s 975us/step - loss: 0.4622 - val_loss: 0.4718
Epoch 82/100
218/218 [==============================] - 0s 949us/step - loss: 0.4629 - val_loss: 0.4725
Epoch 83/100
218/218 [==============================] - 0s 939us/step - loss: 0.4608 - val_loss: 0.4719
Epoch 84/100
218/218 [==============================] - 0s 963us/step - loss: 0.4623 - val_loss: 0.4689
Epoch 85/100
218/218 [==============================] - 0s 957us/step - loss: 0.4603 - val_loss: 0.4725
Epoch 86/100
218/218 [==============================] - 0s 949us/step - loss: 0.4606 - val_loss: 0.4726
Epoch 87/100
218/218 [==============================] - 0s 953us/step - loss: 0.4603 - val_loss: 0.4681
Epoch 88/100
218/218 [==============================] - 0s 955us/step - loss: 0.4622 - val_loss: 0.4721
Epoch 89/100
218/218 [==============================] - 0s 955us/step - loss: 0.4588 - val_loss: 0.4698
Epoch 90/100
218/218 [==============================] - 0s 958us/step - loss: 0.4594 - val_loss: 0.4700
Epoch 91/100
218/218 [==============================] - 0s 962us/step - loss: 0.4610 - val_loss: 0.4693
Epoch 92/100
218/218 [==============================] - 0s 953us/step - loss: 0.4609 - val_loss: 0.4717
Epoch 93/100
218/218 [==============================] - 0s 950us/step - loss: 0.4606 - val_loss: 0.4693
Epoch 94/100
218/218 [==============================] - 0s 955us/step - loss: 0.4617 - val_loss: 0.4659
Epoch 95/100
218/218 [==============================] - 0s 959us/step - loss: 0.4603 - val_loss: 0.4678
Epoch 96/100
218/218 [==============================] - 0s 961us/step - loss: 0.4598 - val_loss: 0.4691
Epoch 97/100
218/218 [==============================] - 0s 960us/step - loss: 0.4600 - val_loss: 0.4712
Epoch 98/100
218/218 [==============================] - 0s 949us/step - loss: 0.4600 - val_loss: 0.4681
Epoch 99/100
218/218 [==============================] - 0s 967us/step - loss: 0.4606 - val_loss: 0.4694
Epoch 100/100
218/218 [==============================] - 0s 948us/step - loss: 0.4595 - val_loss: 0.4678
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.43 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.69 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.85 seconds.
Calculating KMeans...
Calculated KMeans in 16.29 seconds.
Calculated landmark operator in 18.62 seconds.
Calculating optimal t...
Automatically selected t = 43
Calculated optimal t in 2.93 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.71 seconds.
Calculating metric MDS...
Calculated metric MDS in 18.21 seconds.
Calculated PHATE in 54.17 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_323 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_324 (Dense) (None, 32) 2080 dense_323[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_324[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_324[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_325 (Dense) (None, 32) 128
_________________________________________________________________
dense_326 (Dense) (None, 64) 2112
_________________________________________________________________
dense_327 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_323 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_324 (Dense) (None, 32) 2080 dense_323[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_324[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_324[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1070 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1070 [()] 0 tf_op_layer_Shape_1070[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_107 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1070[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_107[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_321 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_107 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_321[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1071 (TensorF [(2,)] 0 tf_op_layer_Add_107[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1073 (TensorF [(2,)] 0 tf_op_layer_Add_107[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1072 (TensorF [(2,)] 0 tf_op_layer_Add_107[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1074 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1076 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1075 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1077 (TensorF [(2,)] 0 tf_op_layer_Add_107[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1079 (TensorF [(2,)] 0 tf_op_layer_Add_107[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1078 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1071 [()] 0 tf_op_layer_Shape_1071[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1073 [()] 0 tf_op_layer_Shape_1073[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1072 [()] 0 tf_op_layer_Shape_1072[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1074 [()] 0 tf_op_layer_Shape_1074[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1076 [()] 0 tf_op_layer_Shape_1076[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1075 [()] 0 tf_op_layer_Shape_1075[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1077 [()] 0 tf_op_layer_Shape_1077[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1079 [()] 0 tf_op_layer_Shape_1079[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1078 [()] 0 tf_op_layer_Shape_1078[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_642/shape ( [(3,)] 0 tf_op_layer_strided_slice_1071[0]
tf_op_layer_strided_slice_1073[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_643/shape ( [(3,)] 0 tf_op_layer_strided_slice_1072[0]
tf_op_layer_strided_slice_1073[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_644/shape ( [(3,)] 0 tf_op_layer_strided_slice_1074[0]
tf_op_layer_strided_slice_1076[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_645/shape ( [(3,)] 0 tf_op_layer_strided_slice_1075[0]
tf_op_layer_strided_slice_1076[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_646/shape ( [(3,)] 0 tf_op_layer_strided_slice_1077[0]
tf_op_layer_strided_slice_1079[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_647/shape ( [(3,)] 0 tf_op_layer_strided_slice_1078[0]
tf_op_layer_strided_slice_1079[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_642 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_107[0][0]
tf_op_layer_Reshape_642/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_642/multiples [(3,)] 0 tf_op_layer_strided_slice_1072[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_643 (Tensor [(1, None, None)] 0 tf_op_layer_Add_107[0][0]
tf_op_layer_Reshape_643/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_643/multiples [(3,)] 0 tf_op_layer_strided_slice_1071[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_644 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_644/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_644/multiples [(3,)] 0 tf_op_layer_strided_slice_1075[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_645 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_645/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_645/multiples [(3,)] 0 tf_op_layer_strided_slice_1074[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_646 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_107[0][0]
tf_op_layer_Reshape_646/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_646/multiples [(3,)] 0 tf_op_layer_strided_slice_1078[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_647 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_647/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_647/multiples [(3,)] 0 tf_op_layer_strided_slice_1077[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_642 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_642[0][0]
tf_op_layer_Tile_642/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_643 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_643[0][0]
tf_op_layer_Tile_643/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_644 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_644[0][0]
tf_op_layer_Tile_644/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_645 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_645[0][0]
tf_op_layer_Tile_645/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_646 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_646[0][0]
tf_op_layer_Tile_646/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_647 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_647[0][0]
tf_op_layer_Tile_647/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_535 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_642[0][0]
tf_op_layer_Tile_643[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_536 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_644[0][0]
tf_op_layer_Tile_645[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_537 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_646[0][0]
tf_op_layer_Tile_647[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_428 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_535[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_429 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_536[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_430 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_537[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_749 (TensorFlo [(None, None)] 0 tf_op_layer_Square_428[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_750 (TensorFlo [(None, None)] 0 tf_op_layer_Square_429[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_751 (TensorFlo [(None, None)] 0 tf_op_layer_Square_430[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_321 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_749[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_321 (TensorFlo [()] 0 tf_op_layer_strided_slice_1073[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_322 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_750[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_322 (TensorFlo [()] 0 tf_op_layer_strided_slice_1076[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_323 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_751[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_323 (TensorFlo [()] 0 tf_op_layer_strided_slice_1079[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_321 (Tensor [(None, None)] 0 tf_op_layer_Neg_321[0][0]
tf_op_layer_Cast_321[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_322 (Tensor [(None, None)] 0 tf_op_layer_Neg_322[0][0]
tf_op_layer_Cast_322[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_323 (Tensor [(None, None)] 0 tf_op_layer_Neg_323[0][0]
tf_op_layer_Cast_323[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_321 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_321[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_322 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_322[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_323 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_323[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_752 (TensorFlo [()] 0 tf_op_layer_Exp_321[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_753 (TensorFlo [()] 0 tf_op_layer_Exp_322[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_754 (TensorFlo [()] 0 tf_op_layer_Exp_323[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_539 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_214 (TensorFl [()] 0 tf_op_layer_Mean_752[0][0]
tf_op_layer_Mean_753[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_322 (TensorFlow [()] 0 tf_op_layer_Mean_754[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_431 (TensorF [(None, 97)] 0 tf_op_layer_Sub_539[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_538 (TensorFlow [()] 0 tf_op_layer_AddV2_214[0][0]
tf_op_layer_Mul_322[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_107 (TensorFlow [(None,)] 0 tf_op_layer_Square_431[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_323 (TensorFlow [()] 0 tf_op_layer_Sub_538[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_215 (TensorFl [(None,)] 0 tf_op_layer_Sum_107[0][0]
tf_op_layer_Mul_323[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_755 (TensorFlo [()] 0 tf_op_layer_AddV2_215[0][0]
__________________________________________________________________________________________________
add_loss_107 (AddLoss) () 0 tf_op_layer_Mean_755[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 8.1246WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.1119s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9442 - val_loss: 0.5080
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4861 - val_loss: 0.4262
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4060 - val_loss: 0.3774
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3901 - val_loss: 0.3722
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3826 - val_loss: 0.3698
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3781 - val_loss: 0.3652
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3759 - val_loss: 0.3617
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3725 - val_loss: 0.3594
Epoch 9/100
218/218 [==============================] - 0s 984us/step - loss: 0.3691 - val_loss: 0.3592
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3668 - val_loss: 0.3550
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3655 - val_loss: 0.3570
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3628 - val_loss: 0.3510
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3632 - val_loss: 0.3505
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3614 - val_loss: 0.3489
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3596 - val_loss: 0.3474
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3581 - val_loss: 0.3501
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3563 - val_loss: 0.3463
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3570 - val_loss: 0.3471
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3490
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3551 - val_loss: 0.3426
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3557 - val_loss: 0.3478
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3550 - val_loss: 0.3440
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3548 - val_loss: 0.3470
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3528 - val_loss: 0.3447
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3516 - val_loss: 0.3421
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3522 - val_loss: 0.3415
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3538 - val_loss: 0.3440
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3517 - val_loss: 0.3419
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3417
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3507 - val_loss: 0.3440
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3510 - val_loss: 0.3402
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3510 - val_loss: 0.3399
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3512 - val_loss: 0.3398
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3500 - val_loss: 0.3441
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3499 - val_loss: 0.3410
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3417
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3425
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3399
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3438
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3483 - val_loss: 0.3413
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3408
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3379
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3482 - val_loss: 0.3404
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3387
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3385
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3381
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3377
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3411
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3420
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3403
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3398
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3393
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3376
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3379
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3413
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3455 - val_loss: 0.3403
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3373
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3382
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3389
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3387
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3412
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3369
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3390
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3384
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3377
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3373
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3363
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3383
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3365
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3396
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3372
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3358
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3377
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3369
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3370
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3394
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3390
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3340
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3387
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3352
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3364
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3376
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3380
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3348
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3410
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3375
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3374
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3354
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3372
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3381
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3373
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3346
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3375
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3374
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3380
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3359
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3362
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3371
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3362
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3355
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_328 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1080 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1080 [()] 0 tf_op_layer_Shape_1080[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_108 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1080[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_108[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_324 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_108 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_324[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1081 (TensorF [(2,)] 0 tf_op_layer_Add_108[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1083 (TensorF [(2,)] 0 tf_op_layer_Add_108[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1082 (TensorF [(2,)] 0 tf_op_layer_Add_108[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1084 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1086 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1085 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1087 (TensorF [(2,)] 0 tf_op_layer_Add_108[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1089 (TensorF [(2,)] 0 tf_op_layer_Add_108[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1088 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1081 [()] 0 tf_op_layer_Shape_1081[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1083 [()] 0 tf_op_layer_Shape_1083[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1082 [()] 0 tf_op_layer_Shape_1082[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1084 [()] 0 tf_op_layer_Shape_1084[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1086 [()] 0 tf_op_layer_Shape_1086[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1085 [()] 0 tf_op_layer_Shape_1085[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1087 [()] 0 tf_op_layer_Shape_1087[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1089 [()] 0 tf_op_layer_Shape_1089[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1088 [()] 0 tf_op_layer_Shape_1088[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_648/shape ( [(3,)] 0 tf_op_layer_strided_slice_1081[0]
tf_op_layer_strided_slice_1083[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_649/shape ( [(3,)] 0 tf_op_layer_strided_slice_1082[0]
tf_op_layer_strided_slice_1083[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_650/shape ( [(3,)] 0 tf_op_layer_strided_slice_1084[0]
tf_op_layer_strided_slice_1086[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_651/shape ( [(3,)] 0 tf_op_layer_strided_slice_1085[0]
tf_op_layer_strided_slice_1086[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_652/shape ( [(3,)] 0 tf_op_layer_strided_slice_1087[0]
tf_op_layer_strided_slice_1089[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_653/shape ( [(3,)] 0 tf_op_layer_strided_slice_1088[0]
tf_op_layer_strided_slice_1089[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_648 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_108[0][0]
tf_op_layer_Reshape_648/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_648/multiples [(3,)] 0 tf_op_layer_strided_slice_1082[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_649 (Tensor [(1, None, None)] 0 tf_op_layer_Add_108[0][0]
tf_op_layer_Reshape_649/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_649/multiples [(3,)] 0 tf_op_layer_strided_slice_1081[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_650 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_650/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_650/multiples [(3,)] 0 tf_op_layer_strided_slice_1085[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_651 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_651/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_651/multiples [(3,)] 0 tf_op_layer_strided_slice_1084[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_652 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_108[0][0]
tf_op_layer_Reshape_652/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_652/multiples [(3,)] 0 tf_op_layer_strided_slice_1088[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_653 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_653/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_653/multiples [(3,)] 0 tf_op_layer_strided_slice_1087[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_648 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_648[0][0]
tf_op_layer_Tile_648/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_649 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_649[0][0]
tf_op_layer_Tile_649/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_650 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_650[0][0]
tf_op_layer_Tile_650/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_651 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_651[0][0]
tf_op_layer_Tile_651/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_652 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_652[0][0]
tf_op_layer_Tile_652/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_653 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_653[0][0]
tf_op_layer_Tile_653/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_540 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_648[0][0]
tf_op_layer_Tile_649[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_541 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_650[0][0]
tf_op_layer_Tile_651[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_542 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_652[0][0]
tf_op_layer_Tile_653[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_432 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_540[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_433 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_541[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_434 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_542[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_756 (TensorFlo [(None, None)] 0 tf_op_layer_Square_432[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_757 (TensorFlo [(None, None)] 0 tf_op_layer_Square_433[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_758 (TensorFlo [(None, None)] 0 tf_op_layer_Square_434[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_324 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_756[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_324 (TensorFlo [()] 0 tf_op_layer_strided_slice_1083[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_325 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_757[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_325 (TensorFlo [()] 0 tf_op_layer_strided_slice_1086[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_326 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_758[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_326 (TensorFlo [()] 0 tf_op_layer_strided_slice_1089[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_324 (Tensor [(None, None)] 0 tf_op_layer_Neg_324[0][0]
tf_op_layer_Cast_324[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_325 (Tensor [(None, None)] 0 tf_op_layer_Neg_325[0][0]
tf_op_layer_Cast_325[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_326 (Tensor [(None, None)] 0 tf_op_layer_Neg_326[0][0]
tf_op_layer_Cast_326[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_324 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_324[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_325 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_325[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_326 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_326[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_759 (TensorFlo [()] 0 tf_op_layer_Exp_324[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_760 (TensorFlo [()] 0 tf_op_layer_Exp_325[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_761 (TensorFlo [()] 0 tf_op_layer_Exp_326[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_544 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_216 (TensorFl [()] 0 tf_op_layer_Mean_759[0][0]
tf_op_layer_Mean_760[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_325 (TensorFlow [()] 0 tf_op_layer_Mean_761[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_435 (TensorF [(None, 97)] 0 tf_op_layer_Sub_544[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_543 (TensorFlow [()] 0 tf_op_layer_AddV2_216[0][0]
tf_op_layer_Mul_325[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_108 (TensorFlow [(None,)] 0 tf_op_layer_Square_435[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_326 (TensorFlow [()] 0 tf_op_layer_Sub_543[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_217 (TensorFl [(None,)] 0 tf_op_layer_Sum_108[0][0]
tf_op_layer_Mul_326[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_762 (TensorFlo [()] 0 tf_op_layer_AddV2_217[0][0]
__________________________________________________________________________________________________
add_loss_108 (AddLoss) () 0 tf_op_layer_Mean_762[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 9s - loss: 7.9804WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0892s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9725 - val_loss: 0.9874
Epoch 2/100
218/218 [==============================] - 0s 907us/step - loss: 0.8911 - val_loss: 0.8148
Epoch 3/100
218/218 [==============================] - 0s 902us/step - loss: 0.7810 - val_loss: 0.7362
Epoch 4/100
218/218 [==============================] - 0s 904us/step - loss: 0.7086 - val_loss: 0.6784
Epoch 5/100
218/218 [==============================] - 0s 901us/step - loss: 0.6536 - val_loss: 0.6316
Epoch 6/100
218/218 [==============================] - 0s 939us/step - loss: 0.6111 - val_loss: 0.5942
Epoch 7/100
218/218 [==============================] - 0s 935us/step - loss: 0.5872 - val_loss: 0.5816
Epoch 8/100
218/218 [==============================] - 0s 918us/step - loss: 0.5692 - val_loss: 0.5650
Epoch 9/100
218/218 [==============================] - 0s 911us/step - loss: 0.5561 - val_loss: 0.5525
Epoch 10/100
218/218 [==============================] - 0s 904us/step - loss: 0.5480 - val_loss: 0.5377
Epoch 11/100
218/218 [==============================] - 0s 907us/step - loss: 0.5433 - val_loss: 0.5366
Epoch 12/100
218/218 [==============================] - 0s 905us/step - loss: 0.5374 - val_loss: 0.5326
Epoch 13/100
218/218 [==============================] - 0s 904us/step - loss: 0.5311 - val_loss: 0.5280
Epoch 14/100
218/218 [==============================] - 0s 908us/step - loss: 0.5220 - val_loss: 0.5261
Epoch 15/100
218/218 [==============================] - 0s 906us/step - loss: 0.5195 - val_loss: 0.5167
Epoch 16/100
218/218 [==============================] - 0s 911us/step - loss: 0.5175 - val_loss: 0.5123
Epoch 17/100
218/218 [==============================] - 0s 920us/step - loss: 0.5123 - val_loss: 0.5120
Epoch 18/100
218/218 [==============================] - 0s 906us/step - loss: 0.5100 - val_loss: 0.5129
Epoch 19/100
218/218 [==============================] - 0s 917us/step - loss: 0.5078 - val_loss: 0.5083
Epoch 20/100
218/218 [==============================] - 0s 918us/step - loss: 0.5076 - val_loss: 0.5122
Epoch 21/100
218/218 [==============================] - 0s 931us/step - loss: 0.5019 - val_loss: 0.5031
Epoch 22/100
218/218 [==============================] - 0s 917us/step - loss: 0.5034 - val_loss: 0.4987
Epoch 23/100
218/218 [==============================] - 0s 863us/step - loss: 0.4977 - val_loss: 0.4963
Epoch 24/100
218/218 [==============================] - 0s 908us/step - loss: 0.4978 - val_loss: 0.4965
Epoch 25/100
218/218 [==============================] - 0s 910us/step - loss: 0.4968 - val_loss: 0.4905
Epoch 26/100
218/218 [==============================] - 0s 907us/step - loss: 0.4969 - val_loss: 0.5019
Epoch 27/100
218/218 [==============================] - 0s 910us/step - loss: 0.4937 - val_loss: 0.4939
Epoch 28/100
218/218 [==============================] - 0s 904us/step - loss: 0.4922 - val_loss: 0.4920
Epoch 29/100
218/218 [==============================] - 0s 910us/step - loss: 0.4914 - val_loss: 0.4974
Epoch 30/100
218/218 [==============================] - 0s 911us/step - loss: 0.4904 - val_loss: 0.4921
Epoch 31/100
218/218 [==============================] - 0s 911us/step - loss: 0.4878 - val_loss: 0.4902
Epoch 32/100
218/218 [==============================] - 0s 915us/step - loss: 0.4881 - val_loss: 0.4888
Epoch 33/100
218/218 [==============================] - 0s 908us/step - loss: 0.4860 - val_loss: 0.4852
Epoch 34/100
218/218 [==============================] - 0s 912us/step - loss: 0.4841 - val_loss: 0.4850
Epoch 35/100
218/218 [==============================] - 0s 909us/step - loss: 0.4823 - val_loss: 0.4897
Epoch 36/100
218/218 [==============================] - 0s 907us/step - loss: 0.4848 - val_loss: 0.4831
Epoch 37/100
218/218 [==============================] - 0s 931us/step - loss: 0.4812 - val_loss: 0.4874
Epoch 38/100
218/218 [==============================] - 0s 933us/step - loss: 0.4827 - val_loss: 0.4812
Epoch 39/100
218/218 [==============================] - 0s 936us/step - loss: 0.4816 - val_loss: 0.4838
Epoch 40/100
218/218 [==============================] - 0s 938us/step - loss: 0.4807 - val_loss: 0.4788
Epoch 41/100
218/218 [==============================] - 0s 941us/step - loss: 0.4812 - val_loss: 0.4787
Epoch 42/100
218/218 [==============================] - 0s 933us/step - loss: 0.4809 - val_loss: 0.4798
Epoch 43/100
218/218 [==============================] - 0s 934us/step - loss: 0.4790 - val_loss: 0.4791
Epoch 44/100
218/218 [==============================] - 0s 938us/step - loss: 0.4776 - val_loss: 0.4785
Epoch 45/100
218/218 [==============================] - 0s 938us/step - loss: 0.4757 - val_loss: 0.4785
Epoch 46/100
218/218 [==============================] - 0s 956us/step - loss: 0.4796 - val_loss: 0.4812
Epoch 47/100
218/218 [==============================] - 0s 947us/step - loss: 0.4773 - val_loss: 0.4791
Epoch 48/100
218/218 [==============================] - 0s 938us/step - loss: 0.4761 - val_loss: 0.4804
Epoch 49/100
218/218 [==============================] - 0s 940us/step - loss: 0.4756 - val_loss: 0.4771
Epoch 50/100
218/218 [==============================] - 0s 940us/step - loss: 0.4751 - val_loss: 0.4793
Epoch 51/100
218/218 [==============================] - 0s 940us/step - loss: 0.4740 - val_loss: 0.4807
Epoch 52/100
218/218 [==============================] - 0s 934us/step - loss: 0.4753 - val_loss: 0.4819
Epoch 53/100
218/218 [==============================] - 0s 956us/step - loss: 0.4726 - val_loss: 0.4724
Epoch 54/100
218/218 [==============================] - 0s 971us/step - loss: 0.4736 - val_loss: 0.4737
Epoch 55/100
218/218 [==============================] - 0s 964us/step - loss: 0.4732 - val_loss: 0.4764
Epoch 56/100
218/218 [==============================] - 0s 979us/step - loss: 0.4734 - val_loss: 0.4736
Epoch 57/100
218/218 [==============================] - 0s 981us/step - loss: 0.4719 - val_loss: 0.4736
Epoch 58/100
218/218 [==============================] - 0s 975us/step - loss: 0.4719 - val_loss: 0.4739
Epoch 59/100
218/218 [==============================] - 0s 970us/step - loss: 0.4743 - val_loss: 0.4760
Epoch 60/100
218/218 [==============================] - 0s 967us/step - loss: 0.4723 - val_loss: 0.4711
Epoch 61/100
218/218 [==============================] - 0s 987us/step - loss: 0.4730 - val_loss: 0.4717
Epoch 62/100
218/218 [==============================] - 0s 967us/step - loss: 0.4703 - val_loss: 0.4729
Epoch 63/100
218/218 [==============================] - 0s 971us/step - loss: 0.4727 - val_loss: 0.4704
Epoch 64/100
218/218 [==============================] - 0s 968us/step - loss: 0.4700 - val_loss: 0.4680
Epoch 65/100
218/218 [==============================] - 0s 967us/step - loss: 0.4698 - val_loss: 0.4705
Epoch 66/100
218/218 [==============================] - 0s 968us/step - loss: 0.4690 - val_loss: 0.4700
Epoch 67/100
218/218 [==============================] - 0s 965us/step - loss: 0.4704 - val_loss: 0.4706
Epoch 68/100
218/218 [==============================] - 0s 963us/step - loss: 0.4692 - val_loss: 0.4725
Epoch 69/100
218/218 [==============================] - 0s 964us/step - loss: 0.4703 - val_loss: 0.4664
Epoch 70/100
218/218 [==============================] - 0s 971us/step - loss: 0.4683 - val_loss: 0.4703
Epoch 71/100
218/218 [==============================] - 0s 968us/step - loss: 0.4684 - val_loss: 0.4685
Epoch 72/100
218/218 [==============================] - 0s 961us/step - loss: 0.4711 - val_loss: 0.4675
Epoch 73/100
218/218 [==============================] - 0s 963us/step - loss: 0.4685 - val_loss: 0.4676
Epoch 74/100
218/218 [==============================] - 0s 970us/step - loss: 0.4684 - val_loss: 0.4669
Epoch 75/100
218/218 [==============================] - 0s 969us/step - loss: 0.4683 - val_loss: 0.4687
Epoch 76/100
218/218 [==============================] - 0s 966us/step - loss: 0.4680 - val_loss: 0.4743
Epoch 77/100
218/218 [==============================] - 0s 971us/step - loss: 0.4660 - val_loss: 0.4692
Epoch 78/100
218/218 [==============================] - 0s 962us/step - loss: 0.4664 - val_loss: 0.4660
Epoch 79/100
218/218 [==============================] - 0s 967us/step - loss: 0.4665 - val_loss: 0.4632
Epoch 80/100
218/218 [==============================] - 0s 968us/step - loss: 0.4682 - val_loss: 0.4685
Epoch 81/100
218/218 [==============================] - 0s 963us/step - loss: 0.4659 - val_loss: 0.4737
Epoch 82/100
218/218 [==============================] - 0s 968us/step - loss: 0.4660 - val_loss: 0.4736
Epoch 83/100
218/218 [==============================] - 0s 965us/step - loss: 0.4662 - val_loss: 0.4685
Epoch 84/100
218/218 [==============================] - 0s 972us/step - loss: 0.4657 - val_loss: 0.4692
Epoch 85/100
218/218 [==============================] - 0s 990us/step - loss: 0.4654 - val_loss: 0.4668
Epoch 86/100
218/218 [==============================] - 0s 972us/step - loss: 0.4638 - val_loss: 0.4644
Epoch 87/100
218/218 [==============================] - 0s 971us/step - loss: 0.4651 - val_loss: 0.4694
Epoch 88/100
218/218 [==============================] - 0s 962us/step - loss: 0.4657 - val_loss: 0.4668
Epoch 89/100
218/218 [==============================] - 0s 970us/step - loss: 0.4643 - val_loss: 0.4683
Epoch 90/100
218/218 [==============================] - 0s 962us/step - loss: 0.4642 - val_loss: 0.4647
Epoch 91/100
218/218 [==============================] - 0s 977us/step - loss: 0.4644 - val_loss: 0.4684
Epoch 92/100
218/218 [==============================] - 0s 969us/step - loss: 0.4646 - val_loss: 0.4681
Epoch 93/100
218/218 [==============================] - 0s 951us/step - loss: 0.4632 - val_loss: 0.4646
Epoch 94/100
218/218 [==============================] - 0s 957us/step - loss: 0.4646 - val_loss: 0.4626
Epoch 95/100
218/218 [==============================] - 0s 965us/step - loss: 0.4647 - val_loss: 0.4672
Epoch 96/100
218/218 [==============================] - 0s 969us/step - loss: 0.4647 - val_loss: 0.4667
Epoch 97/100
218/218 [==============================] - 0s 988us/step - loss: 0.4641 - val_loss: 0.4652
Epoch 98/100
218/218 [==============================] - 0s 973us/step - loss: 0.4632 - val_loss: 0.4669
Epoch 99/100
218/218 [==============================] - 0s 971us/step - loss: 0.4620 - val_loss: 0.4652
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4646 - val_loss: 0.4637
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.25 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.52 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.85 seconds.
Calculating KMeans...
Calculated KMeans in 12.37 seconds.
Calculated landmark operator in 14.70 seconds.
Calculating optimal t...
Automatically selected t = 27
Calculated optimal t in 2.95 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.61 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.17 seconds.
Calculated PHATE in 47.97 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_329 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_330 (Dense) (None, 32) 2080 dense_329[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_330[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_330[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_331 (Dense) (None, 32) 128
_________________________________________________________________
dense_332 (Dense) (None, 64) 2112
_________________________________________________________________
dense_333 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_329 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_330 (Dense) (None, 32) 2080 dense_329[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_330[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_330[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1090 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1090 [()] 0 tf_op_layer_Shape_1090[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_109 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1090[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_109[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_327 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_109 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_327[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1091 (TensorF [(2,)] 0 tf_op_layer_Add_109[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1093 (TensorF [(2,)] 0 tf_op_layer_Add_109[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1092 (TensorF [(2,)] 0 tf_op_layer_Add_109[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1094 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1096 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1095 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1097 (TensorF [(2,)] 0 tf_op_layer_Add_109[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1099 (TensorF [(2,)] 0 tf_op_layer_Add_109[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1098 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1091 [()] 0 tf_op_layer_Shape_1091[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1093 [()] 0 tf_op_layer_Shape_1093[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1092 [()] 0 tf_op_layer_Shape_1092[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1094 [()] 0 tf_op_layer_Shape_1094[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1096 [()] 0 tf_op_layer_Shape_1096[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1095 [()] 0 tf_op_layer_Shape_1095[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1097 [()] 0 tf_op_layer_Shape_1097[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1099 [()] 0 tf_op_layer_Shape_1099[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1098 [()] 0 tf_op_layer_Shape_1098[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_654/shape ( [(3,)] 0 tf_op_layer_strided_slice_1091[0]
tf_op_layer_strided_slice_1093[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_655/shape ( [(3,)] 0 tf_op_layer_strided_slice_1092[0]
tf_op_layer_strided_slice_1093[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_656/shape ( [(3,)] 0 tf_op_layer_strided_slice_1094[0]
tf_op_layer_strided_slice_1096[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_657/shape ( [(3,)] 0 tf_op_layer_strided_slice_1095[0]
tf_op_layer_strided_slice_1096[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_658/shape ( [(3,)] 0 tf_op_layer_strided_slice_1097[0]
tf_op_layer_strided_slice_1099[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_659/shape ( [(3,)] 0 tf_op_layer_strided_slice_1098[0]
tf_op_layer_strided_slice_1099[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_654 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_109[0][0]
tf_op_layer_Reshape_654/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_654/multiples [(3,)] 0 tf_op_layer_strided_slice_1092[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_655 (Tensor [(1, None, None)] 0 tf_op_layer_Add_109[0][0]
tf_op_layer_Reshape_655/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_655/multiples [(3,)] 0 tf_op_layer_strided_slice_1091[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_656 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_656/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_656/multiples [(3,)] 0 tf_op_layer_strided_slice_1095[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_657 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_657/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_657/multiples [(3,)] 0 tf_op_layer_strided_slice_1094[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_658 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_109[0][0]
tf_op_layer_Reshape_658/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_658/multiples [(3,)] 0 tf_op_layer_strided_slice_1098[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_659 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_659/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_659/multiples [(3,)] 0 tf_op_layer_strided_slice_1097[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_654 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_654[0][0]
tf_op_layer_Tile_654/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_655 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_655[0][0]
tf_op_layer_Tile_655/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_656 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_656[0][0]
tf_op_layer_Tile_656/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_657 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_657[0][0]
tf_op_layer_Tile_657/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_658 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_658[0][0]
tf_op_layer_Tile_658/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_659 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_659[0][0]
tf_op_layer_Tile_659/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_545 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_654[0][0]
tf_op_layer_Tile_655[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_546 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_656[0][0]
tf_op_layer_Tile_657[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_547 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_658[0][0]
tf_op_layer_Tile_659[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_436 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_545[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_437 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_546[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_438 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_547[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_763 (TensorFlo [(None, None)] 0 tf_op_layer_Square_436[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_764 (TensorFlo [(None, None)] 0 tf_op_layer_Square_437[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_765 (TensorFlo [(None, None)] 0 tf_op_layer_Square_438[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_327 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_763[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_327 (TensorFlo [()] 0 tf_op_layer_strided_slice_1093[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_328 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_764[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_328 (TensorFlo [()] 0 tf_op_layer_strided_slice_1096[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_329 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_765[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_329 (TensorFlo [()] 0 tf_op_layer_strided_slice_1099[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_327 (Tensor [(None, None)] 0 tf_op_layer_Neg_327[0][0]
tf_op_layer_Cast_327[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_328 (Tensor [(None, None)] 0 tf_op_layer_Neg_328[0][0]
tf_op_layer_Cast_328[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_329 (Tensor [(None, None)] 0 tf_op_layer_Neg_329[0][0]
tf_op_layer_Cast_329[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_327 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_327[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_328 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_328[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_329 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_329[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_766 (TensorFlo [()] 0 tf_op_layer_Exp_327[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_767 (TensorFlo [()] 0 tf_op_layer_Exp_328[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_768 (TensorFlo [()] 0 tf_op_layer_Exp_329[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_549 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_218 (TensorFl [()] 0 tf_op_layer_Mean_766[0][0]
tf_op_layer_Mean_767[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_328 (TensorFlow [()] 0 tf_op_layer_Mean_768[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_439 (TensorF [(None, 97)] 0 tf_op_layer_Sub_549[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_548 (TensorFlow [()] 0 tf_op_layer_AddV2_218[0][0]
tf_op_layer_Mul_328[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_109 (TensorFlow [(None,)] 0 tf_op_layer_Square_439[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_329 (TensorFlow [()] 0 tf_op_layer_Sub_548[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_219 (TensorFl [(None,)] 0 tf_op_layer_Sum_109[0][0]
tf_op_layer_Mul_329[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_769 (TensorFlo [()] 0 tf_op_layer_AddV2_219[0][0]
__________________________________________________________________________________________________
add_loss_109 (AddLoss) () 0 tf_op_layer_Mean_769[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 13s - loss: 7.0135WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1271s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9374 - val_loss: 0.5140
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4766 - val_loss: 0.4106
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4005 - val_loss: 0.3789
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3858 - val_loss: 0.3698
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3795 - val_loss: 0.3695
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3777 - val_loss: 0.3682
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3736 - val_loss: 0.3621
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3715 - val_loss: 0.3621
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3682 - val_loss: 0.3581
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3669 - val_loss: 0.3558
Epoch 11/100
218/218 [==============================] - 0s 948us/step - loss: 0.3655 - val_loss: 0.3512
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3621 - val_loss: 0.3492
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3618 - val_loss: 0.3491
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3610 - val_loss: 0.3463
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3590 - val_loss: 0.3484
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3578 - val_loss: 0.3475
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3596 - val_loss: 0.3456
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3560 - val_loss: 0.3466
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3556 - val_loss: 0.3453
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3557 - val_loss: 0.3436
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3550 - val_loss: 0.3450
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3534 - val_loss: 0.3442
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3555 - val_loss: 0.3423
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3539 - val_loss: 0.3413
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3514 - val_loss: 0.3416
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3516 - val_loss: 0.3424
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3523 - val_loss: 0.3408
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3525 - val_loss: 0.3381
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3508 - val_loss: 0.3401
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3515 - val_loss: 0.3423
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3506 - val_loss: 0.3403
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3399
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3515 - val_loss: 0.3397
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3408
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3497 - val_loss: 0.3393
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3495 - val_loss: 0.3405
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3371
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3369
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3371
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3349
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3396
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3375
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3389
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3367
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3395
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3397
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3394
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3366
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3362
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3398
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3357
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3383
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3386
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3345
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3406
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3353
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3380
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3358
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3346
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3372
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3371
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3360
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3364
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3365
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3364
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3363
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3360
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3361
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3334
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3377
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3332
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3357
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3376
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3362
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3344
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3353
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3371
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3363
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3349
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3365
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3345
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3360
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3344
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3358
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3364
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3345
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3359
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3342
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3343
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3354
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3353
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3335
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3320
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3347
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3329
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3350
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3350
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3385
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3347
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3333
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_334 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1100 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1100 [()] 0 tf_op_layer_Shape_1100[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_110 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1100[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_110[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_330 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_110 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_330[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1101 (TensorF [(2,)] 0 tf_op_layer_Add_110[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1103 (TensorF [(2,)] 0 tf_op_layer_Add_110[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1102 (TensorF [(2,)] 0 tf_op_layer_Add_110[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1104 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1106 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1105 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1107 (TensorF [(2,)] 0 tf_op_layer_Add_110[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1109 (TensorF [(2,)] 0 tf_op_layer_Add_110[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1108 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1101 [()] 0 tf_op_layer_Shape_1101[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1103 [()] 0 tf_op_layer_Shape_1103[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1102 [()] 0 tf_op_layer_Shape_1102[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1104 [()] 0 tf_op_layer_Shape_1104[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1106 [()] 0 tf_op_layer_Shape_1106[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1105 [()] 0 tf_op_layer_Shape_1105[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1107 [()] 0 tf_op_layer_Shape_1107[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1109 [()] 0 tf_op_layer_Shape_1109[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1108 [()] 0 tf_op_layer_Shape_1108[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_660/shape ( [(3,)] 0 tf_op_layer_strided_slice_1101[0]
tf_op_layer_strided_slice_1103[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_661/shape ( [(3,)] 0 tf_op_layer_strided_slice_1102[0]
tf_op_layer_strided_slice_1103[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_662/shape ( [(3,)] 0 tf_op_layer_strided_slice_1104[0]
tf_op_layer_strided_slice_1106[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_663/shape ( [(3,)] 0 tf_op_layer_strided_slice_1105[0]
tf_op_layer_strided_slice_1106[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_664/shape ( [(3,)] 0 tf_op_layer_strided_slice_1107[0]
tf_op_layer_strided_slice_1109[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_665/shape ( [(3,)] 0 tf_op_layer_strided_slice_1108[0]
tf_op_layer_strided_slice_1109[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_660 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_110[0][0]
tf_op_layer_Reshape_660/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_660/multiples [(3,)] 0 tf_op_layer_strided_slice_1102[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_661 (Tensor [(1, None, None)] 0 tf_op_layer_Add_110[0][0]
tf_op_layer_Reshape_661/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_661/multiples [(3,)] 0 tf_op_layer_strided_slice_1101[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_662 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_662/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_662/multiples [(3,)] 0 tf_op_layer_strided_slice_1105[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_663 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_663/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_663/multiples [(3,)] 0 tf_op_layer_strided_slice_1104[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_664 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_110[0][0]
tf_op_layer_Reshape_664/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_664/multiples [(3,)] 0 tf_op_layer_strided_slice_1108[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_665 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_665/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_665/multiples [(3,)] 0 tf_op_layer_strided_slice_1107[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_660 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_660[0][0]
tf_op_layer_Tile_660/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_661 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_661[0][0]
tf_op_layer_Tile_661/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_662 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_662[0][0]
tf_op_layer_Tile_662/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_663 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_663[0][0]
tf_op_layer_Tile_663/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_664 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_664[0][0]
tf_op_layer_Tile_664/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_665 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_665[0][0]
tf_op_layer_Tile_665/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_550 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_660[0][0]
tf_op_layer_Tile_661[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_551 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_662[0][0]
tf_op_layer_Tile_663[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_552 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_664[0][0]
tf_op_layer_Tile_665[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_440 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_550[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_441 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_551[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_442 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_552[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_770 (TensorFlo [(None, None)] 0 tf_op_layer_Square_440[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_771 (TensorFlo [(None, None)] 0 tf_op_layer_Square_441[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_772 (TensorFlo [(None, None)] 0 tf_op_layer_Square_442[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_330 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_770[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_330 (TensorFlo [()] 0 tf_op_layer_strided_slice_1103[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_331 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_771[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_331 (TensorFlo [()] 0 tf_op_layer_strided_slice_1106[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_332 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_772[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_332 (TensorFlo [()] 0 tf_op_layer_strided_slice_1109[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_330 (Tensor [(None, None)] 0 tf_op_layer_Neg_330[0][0]
tf_op_layer_Cast_330[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_331 (Tensor [(None, None)] 0 tf_op_layer_Neg_331[0][0]
tf_op_layer_Cast_331[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_332 (Tensor [(None, None)] 0 tf_op_layer_Neg_332[0][0]
tf_op_layer_Cast_332[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_330 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_330[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_331 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_331[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_332 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_332[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_773 (TensorFlo [()] 0 tf_op_layer_Exp_330[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_774 (TensorFlo [()] 0 tf_op_layer_Exp_331[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_775 (TensorFlo [()] 0 tf_op_layer_Exp_332[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_554 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_220 (TensorFl [()] 0 tf_op_layer_Mean_773[0][0]
tf_op_layer_Mean_774[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_331 (TensorFlow [()] 0 tf_op_layer_Mean_775[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_443 (TensorF [(None, 97)] 0 tf_op_layer_Sub_554[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_553 (TensorFlow [()] 0 tf_op_layer_AddV2_220[0][0]
tf_op_layer_Mul_331[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_110 (TensorFlow [(None,)] 0 tf_op_layer_Square_443[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_332 (TensorFlow [()] 0 tf_op_layer_Sub_553[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_221 (TensorFl [(None,)] 0 tf_op_layer_Sum_110[0][0]
tf_op_layer_Mul_332[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_776 (TensorFlo [()] 0 tf_op_layer_AddV2_221[0][0]
__________________________________________________________________________________________________
add_loss_110 (AddLoss) () 0 tf_op_layer_Mean_776[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 13s - loss: 8.4321WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.1199s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 2.0623 - val_loss: 1.0098
Epoch 2/100
218/218 [==============================] - 0s 922us/step - loss: 0.8996 - val_loss: 0.7756
Epoch 3/100
218/218 [==============================] - 0s 927us/step - loss: 0.7298 - val_loss: 0.6692
Epoch 4/100
218/218 [==============================] - 0s 933us/step - loss: 0.6543 - val_loss: 0.6095
Epoch 5/100
218/218 [==============================] - 0s 924us/step - loss: 0.6182 - val_loss: 0.6001
Epoch 6/100
218/218 [==============================] - 0s 927us/step - loss: 0.5945 - val_loss: 0.5774
Epoch 7/100
218/218 [==============================] - 0s 924us/step - loss: 0.5779 - val_loss: 0.5605
Epoch 8/100
218/218 [==============================] - 0s 926us/step - loss: 0.5651 - val_loss: 0.5422
Epoch 9/100
218/218 [==============================] - 0s 922us/step - loss: 0.5535 - val_loss: 0.5399
Epoch 10/100
218/218 [==============================] - 0s 926us/step - loss: 0.5463 - val_loss: 0.5319
Epoch 11/100
218/218 [==============================] - 0s 924us/step - loss: 0.5379 - val_loss: 0.5195
Epoch 12/100
218/218 [==============================] - 0s 925us/step - loss: 0.5322 - val_loss: 0.5140
Epoch 13/100
218/218 [==============================] - 0s 920us/step - loss: 0.5239 - val_loss: 0.5112
Epoch 14/100
218/218 [==============================] - 0s 928us/step - loss: 0.5237 - val_loss: 0.5055
Epoch 15/100
218/218 [==============================] - 0s 930us/step - loss: 0.5179 - val_loss: 0.4987
Epoch 16/100
218/218 [==============================] - 0s 924us/step - loss: 0.5150 - val_loss: 0.4907
Epoch 17/100
218/218 [==============================] - 0s 929us/step - loss: 0.5128 - val_loss: 0.4929
Epoch 18/100
218/218 [==============================] - 0s 935us/step - loss: 0.5087 - val_loss: 0.4954
Epoch 19/100
218/218 [==============================] - 0s 930us/step - loss: 0.5050 - val_loss: 0.4835
Epoch 20/100
218/218 [==============================] - 0s 928us/step - loss: 0.5004 - val_loss: 0.4850
Epoch 21/100
218/218 [==============================] - 0s 932us/step - loss: 0.4980 - val_loss: 0.4813
Epoch 22/100
218/218 [==============================] - 0s 931us/step - loss: 0.4984 - val_loss: 0.4811
Epoch 23/100
218/218 [==============================] - 0s 929us/step - loss: 0.4973 - val_loss: 0.4751
Epoch 24/100
218/218 [==============================] - 0s 927us/step - loss: 0.4922 - val_loss: 0.4724
Epoch 25/100
218/218 [==============================] - 0s 935us/step - loss: 0.4915 - val_loss: 0.4780
Epoch 26/100
218/218 [==============================] - 0s 929us/step - loss: 0.4911 - val_loss: 0.4764
Epoch 27/100
218/218 [==============================] - 0s 937us/step - loss: 0.4905 - val_loss: 0.4712
Epoch 28/100
218/218 [==============================] - 0s 928us/step - loss: 0.4901 - val_loss: 0.4675
Epoch 29/100
218/218 [==============================] - 0s 928us/step - loss: 0.4846 - val_loss: 0.4701
Epoch 30/100
218/218 [==============================] - 0s 933us/step - loss: 0.4873 - val_loss: 0.4737
Epoch 31/100
218/218 [==============================] - 0s 923us/step - loss: 0.4841 - val_loss: 0.4682
Epoch 32/100
218/218 [==============================] - 0s 919us/step - loss: 0.4849 - val_loss: 0.4656
Epoch 33/100
218/218 [==============================] - 0s 921us/step - loss: 0.4826 - val_loss: 0.4610
Epoch 34/100
218/218 [==============================] - 0s 924us/step - loss: 0.4821 - val_loss: 0.4569
Epoch 35/100
218/218 [==============================] - 0s 925us/step - loss: 0.4806 - val_loss: 0.4604
Epoch 36/100
218/218 [==============================] - 0s 927us/step - loss: 0.4822 - val_loss: 0.4573
Epoch 37/100
218/218 [==============================] - 0s 929us/step - loss: 0.4779 - val_loss: 0.4634
Epoch 38/100
218/218 [==============================] - 0s 887us/step - loss: 0.4785 - val_loss: 0.4576
Epoch 39/100
218/218 [==============================] - 0s 931us/step - loss: 0.4773 - val_loss: 0.4551
Epoch 40/100
218/218 [==============================] - 0s 942us/step - loss: 0.4754 - val_loss: 0.4591
Epoch 41/100
218/218 [==============================] - 0s 944us/step - loss: 0.4761 - val_loss: 0.4539
Epoch 42/100
218/218 [==============================] - 0s 941us/step - loss: 0.4772 - val_loss: 0.4568
Epoch 43/100
218/218 [==============================] - 0s 937us/step - loss: 0.4766 - val_loss: 0.4587
Epoch 44/100
218/218 [==============================] - 0s 930us/step - loss: 0.4753 - val_loss: 0.4566
Epoch 45/100
218/218 [==============================] - 0s 927us/step - loss: 0.4741 - val_loss: 0.4561
Epoch 46/100
218/218 [==============================] - 0s 928us/step - loss: 0.4734 - val_loss: 0.4590
Epoch 47/100
218/218 [==============================] - 0s 924us/step - loss: 0.4733 - val_loss: 0.4568
Epoch 48/100
218/218 [==============================] - 0s 918us/step - loss: 0.4714 - val_loss: 0.4513
Epoch 49/100
218/218 [==============================] - 0s 935us/step - loss: 0.4731 - val_loss: 0.4532
Epoch 50/100
218/218 [==============================] - 0s 937us/step - loss: 0.4712 - val_loss: 0.4536
Epoch 51/100
218/218 [==============================] - 0s 949us/step - loss: 0.4723 - val_loss: 0.4558
Epoch 52/100
218/218 [==============================] - 0s 945us/step - loss: 0.4705 - val_loss: 0.4560
Epoch 53/100
218/218 [==============================] - 0s 945us/step - loss: 0.4715 - val_loss: 0.4526
Epoch 54/100
218/218 [==============================] - 0s 943us/step - loss: 0.4712 - val_loss: 0.4545
Epoch 55/100
218/218 [==============================] - 0s 938us/step - loss: 0.4696 - val_loss: 0.4496
Epoch 56/100
218/218 [==============================] - 0s 922us/step - loss: 0.4728 - val_loss: 0.4535
Epoch 57/100
218/218 [==============================] - 0s 931us/step - loss: 0.4690 - val_loss: 0.4500
Epoch 58/100
218/218 [==============================] - 0s 924us/step - loss: 0.4699 - val_loss: 0.4529
Epoch 59/100
218/218 [==============================] - 0s 927us/step - loss: 0.4691 - val_loss: 0.4519
Epoch 60/100
218/218 [==============================] - 0s 925us/step - loss: 0.4693 - val_loss: 0.4492
Epoch 61/100
218/218 [==============================] - 0s 929us/step - loss: 0.4683 - val_loss: 0.4500
Epoch 62/100
218/218 [==============================] - 0s 923us/step - loss: 0.4690 - val_loss: 0.4489
Epoch 63/100
218/218 [==============================] - 0s 929us/step - loss: 0.4661 - val_loss: 0.4487
Epoch 64/100
218/218 [==============================] - 0s 942us/step - loss: 0.4676 - val_loss: 0.4459
Epoch 65/100
218/218 [==============================] - 0s 924us/step - loss: 0.4667 - val_loss: 0.4524
Epoch 66/100
218/218 [==============================] - 0s 937us/step - loss: 0.4652 - val_loss: 0.4527
Epoch 67/100
218/218 [==============================] - 0s 925us/step - loss: 0.4660 - val_loss: 0.4482
Epoch 68/100
218/218 [==============================] - 0s 947us/step - loss: 0.4669 - val_loss: 0.4490
Epoch 69/100
218/218 [==============================] - 0s 934us/step - loss: 0.4669 - val_loss: 0.4501
Epoch 70/100
218/218 [==============================] - 0s 919us/step - loss: 0.4652 - val_loss: 0.4478
Epoch 71/100
218/218 [==============================] - 0s 923us/step - loss: 0.4667 - val_loss: 0.4472
Epoch 72/100
218/218 [==============================] - 0s 921us/step - loss: 0.4651 - val_loss: 0.4464
Epoch 73/100
218/218 [==============================] - 0s 920us/step - loss: 0.4652 - val_loss: 0.4491
Epoch 74/100
218/218 [==============================] - 0s 928us/step - loss: 0.4657 - val_loss: 0.4420
Epoch 75/100
218/218 [==============================] - 0s 918us/step - loss: 0.4649 - val_loss: 0.4475
Epoch 76/100
218/218 [==============================] - 0s 919us/step - loss: 0.4646 - val_loss: 0.4450
Epoch 77/100
218/218 [==============================] - 0s 916us/step - loss: 0.4627 - val_loss: 0.4457
Epoch 78/100
218/218 [==============================] - 0s 947us/step - loss: 0.4653 - val_loss: 0.4478
Epoch 79/100
218/218 [==============================] - 0s 952us/step - loss: 0.4642 - val_loss: 0.4425
Epoch 80/100
218/218 [==============================] - 0s 949us/step - loss: 0.4644 - val_loss: 0.4468
Epoch 81/100
218/218 [==============================] - 0s 955us/step - loss: 0.4614 - val_loss: 0.4486
Epoch 82/100
218/218 [==============================] - 0s 969us/step - loss: 0.4642 - val_loss: 0.4474
Epoch 83/100
218/218 [==============================] - 0s 960us/step - loss: 0.4660 - val_loss: 0.4461
Epoch 84/100
218/218 [==============================] - 0s 954us/step - loss: 0.4622 - val_loss: 0.4411
Epoch 85/100
218/218 [==============================] - 0s 949us/step - loss: 0.4634 - val_loss: 0.4466
Epoch 86/100
218/218 [==============================] - 0s 952us/step - loss: 0.4603 - val_loss: 0.4442
Epoch 87/100
218/218 [==============================] - 0s 952us/step - loss: 0.4644 - val_loss: 0.4508
Epoch 88/100
218/218 [==============================] - 0s 957us/step - loss: 0.4627 - val_loss: 0.4473
Epoch 89/100
218/218 [==============================] - 0s 954us/step - loss: 0.4613 - val_loss: 0.4479
Epoch 90/100
218/218 [==============================] - 0s 953us/step - loss: 0.4619 - val_loss: 0.4441
Epoch 91/100
218/218 [==============================] - 0s 953us/step - loss: 0.4614 - val_loss: 0.4448
Epoch 92/100
218/218 [==============================] - 0s 951us/step - loss: 0.4644 - val_loss: 0.4437
Epoch 93/100
218/218 [==============================] - 0s 954us/step - loss: 0.4615 - val_loss: 0.4429
Epoch 94/100
218/218 [==============================] - 0s 947us/step - loss: 0.4601 - val_loss: 0.4419
Epoch 95/100
218/218 [==============================] - 0s 955us/step - loss: 0.4615 - val_loss: 0.4421
Epoch 96/100
218/218 [==============================] - 0s 969us/step - loss: 0.4592 - val_loss: 0.4417
Epoch 97/100
218/218 [==============================] - 0s 958us/step - loss: 0.4619 - val_loss: 0.4458
Epoch 98/100
218/218 [==============================] - 0s 964us/step - loss: 0.4604 - val_loss: 0.4428
Epoch 99/100
218/218 [==============================] - 0s 952us/step - loss: 0.4600 - val_loss: 0.4443
Epoch 100/100
218/218 [==============================] - 0s 950us/step - loss: 0.4611 - val_loss: 0.4422
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.04 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.31 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.86 seconds.
Calculating KMeans...
Calculated KMeans in 16.01 seconds.
Calculated landmark operator in 18.32 seconds.
Calculating optimal t...
Automatically selected t = 43
Calculated optimal t in 2.86 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.68 seconds.
Calculating metric MDS...
Calculated metric MDS in 18.23 seconds.
Calculated PHATE in 53.40 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_335 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_336 (Dense) (None, 32) 2080 dense_335[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_336[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_336[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_337 (Dense) (None, 32) 128
_________________________________________________________________
dense_338 (Dense) (None, 64) 2112
_________________________________________________________________
dense_339 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_335 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_336 (Dense) (None, 32) 2080 dense_335[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_336[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_336[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1110 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1110 [()] 0 tf_op_layer_Shape_1110[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_111 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1110[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_111[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_333 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_111 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_333[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1111 (TensorF [(2,)] 0 tf_op_layer_Add_111[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1113 (TensorF [(2,)] 0 tf_op_layer_Add_111[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1112 (TensorF [(2,)] 0 tf_op_layer_Add_111[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1114 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1116 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1115 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1117 (TensorF [(2,)] 0 tf_op_layer_Add_111[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1119 (TensorF [(2,)] 0 tf_op_layer_Add_111[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1118 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1111 [()] 0 tf_op_layer_Shape_1111[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1113 [()] 0 tf_op_layer_Shape_1113[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1112 [()] 0 tf_op_layer_Shape_1112[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1114 [()] 0 tf_op_layer_Shape_1114[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1116 [()] 0 tf_op_layer_Shape_1116[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1115 [()] 0 tf_op_layer_Shape_1115[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1117 [()] 0 tf_op_layer_Shape_1117[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1119 [()] 0 tf_op_layer_Shape_1119[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1118 [()] 0 tf_op_layer_Shape_1118[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_666/shape ( [(3,)] 0 tf_op_layer_strided_slice_1111[0]
tf_op_layer_strided_slice_1113[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_667/shape ( [(3,)] 0 tf_op_layer_strided_slice_1112[0]
tf_op_layer_strided_slice_1113[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_668/shape ( [(3,)] 0 tf_op_layer_strided_slice_1114[0]
tf_op_layer_strided_slice_1116[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_669/shape ( [(3,)] 0 tf_op_layer_strided_slice_1115[0]
tf_op_layer_strided_slice_1116[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_670/shape ( [(3,)] 0 tf_op_layer_strided_slice_1117[0]
tf_op_layer_strided_slice_1119[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_671/shape ( [(3,)] 0 tf_op_layer_strided_slice_1118[0]
tf_op_layer_strided_slice_1119[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_666 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_111[0][0]
tf_op_layer_Reshape_666/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_666/multiples [(3,)] 0 tf_op_layer_strided_slice_1112[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_667 (Tensor [(1, None, None)] 0 tf_op_layer_Add_111[0][0]
tf_op_layer_Reshape_667/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_667/multiples [(3,)] 0 tf_op_layer_strided_slice_1111[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_668 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_668/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_668/multiples [(3,)] 0 tf_op_layer_strided_slice_1115[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_669 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_669/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_669/multiples [(3,)] 0 tf_op_layer_strided_slice_1114[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_670 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_111[0][0]
tf_op_layer_Reshape_670/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_670/multiples [(3,)] 0 tf_op_layer_strided_slice_1118[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_671 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_671/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_671/multiples [(3,)] 0 tf_op_layer_strided_slice_1117[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_666 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_666[0][0]
tf_op_layer_Tile_666/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_667 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_667[0][0]
tf_op_layer_Tile_667/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_668 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_668[0][0]
tf_op_layer_Tile_668/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_669 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_669[0][0]
tf_op_layer_Tile_669/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_670 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_670[0][0]
tf_op_layer_Tile_670/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_671 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_671[0][0]
tf_op_layer_Tile_671/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_555 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_666[0][0]
tf_op_layer_Tile_667[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_556 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_668[0][0]
tf_op_layer_Tile_669[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_557 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_670[0][0]
tf_op_layer_Tile_671[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_444 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_555[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_445 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_556[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_446 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_557[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_777 (TensorFlo [(None, None)] 0 tf_op_layer_Square_444[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_778 (TensorFlo [(None, None)] 0 tf_op_layer_Square_445[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_779 (TensorFlo [(None, None)] 0 tf_op_layer_Square_446[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_333 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_777[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_333 (TensorFlo [()] 0 tf_op_layer_strided_slice_1113[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_334 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_778[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_334 (TensorFlo [()] 0 tf_op_layer_strided_slice_1116[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_335 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_779[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_335 (TensorFlo [()] 0 tf_op_layer_strided_slice_1119[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_333 (Tensor [(None, None)] 0 tf_op_layer_Neg_333[0][0]
tf_op_layer_Cast_333[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_334 (Tensor [(None, None)] 0 tf_op_layer_Neg_334[0][0]
tf_op_layer_Cast_334[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_335 (Tensor [(None, None)] 0 tf_op_layer_Neg_335[0][0]
tf_op_layer_Cast_335[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_333 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_333[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_334 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_334[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_335 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_335[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_780 (TensorFlo [()] 0 tf_op_layer_Exp_333[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_781 (TensorFlo [()] 0 tf_op_layer_Exp_334[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_782 (TensorFlo [()] 0 tf_op_layer_Exp_335[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_559 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_222 (TensorFl [()] 0 tf_op_layer_Mean_780[0][0]
tf_op_layer_Mean_781[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_334 (TensorFlow [()] 0 tf_op_layer_Mean_782[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_447 (TensorF [(None, 97)] 0 tf_op_layer_Sub_559[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_558 (TensorFlow [()] 0 tf_op_layer_AddV2_222[0][0]
tf_op_layer_Mul_334[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_111 (TensorFlow [(None,)] 0 tf_op_layer_Square_447[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_335 (TensorFlow [()] 0 tf_op_layer_Sub_558[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_223 (TensorFl [(None,)] 0 tf_op_layer_Sum_111[0][0]
tf_op_layer_Mul_335[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_783 (TensorFlo [()] 0 tf_op_layer_AddV2_223[0][0]
__________________________________________________________________________________________________
add_loss_111 (AddLoss) () 0 tf_op_layer_Mean_783[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.9405WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0933s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.8658 - val_loss: 0.4919
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4332 - val_loss: 0.4057
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3890 - val_loss: 0.3916
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3782 - val_loss: 0.3890
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3727 - val_loss: 0.3823
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3710 - val_loss: 0.3749
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3658 - val_loss: 0.3728
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3649 - val_loss: 0.3749
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3625 - val_loss: 0.3718
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3594 - val_loss: 0.3674
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3583 - val_loss: 0.3672
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3565 - val_loss: 0.3653
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3555 - val_loss: 0.3647
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3548 - val_loss: 0.3626
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3652
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3538 - val_loss: 0.3629
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3514 - val_loss: 0.3644
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3509 - val_loss: 0.3614
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3508 - val_loss: 0.3605
Epoch 20/100
218/218 [==============================] - 0s 997us/step - loss: 0.3496 - val_loss: 0.3609
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3595
Epoch 22/100
218/218 [==============================] - 0s 987us/step - loss: 0.3489 - val_loss: 0.3657
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3591
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3624
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3586
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3584
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3586
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3590
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3587
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3562
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3591
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3557
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3584
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3573
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3555
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3575
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3584
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3543
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3579
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3530
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3550
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3571
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3560
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3559
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3579
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3579
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3553
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3585
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3573
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3526
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3551
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3539
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3539
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3545
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3567
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3561
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3581
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3405 - val_loss: 0.3549
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3560
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3556
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3401 - val_loss: 0.3549
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3545
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3564
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3531
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3528
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3526
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3542
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3531
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3401 - val_loss: 0.3553
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3523
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3405 - val_loss: 0.3505
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3537
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3518
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3387 - val_loss: 0.3555
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3528
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3387 - val_loss: 0.3544
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3390 - val_loss: 0.3513
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3533
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3388 - val_loss: 0.3537
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3551
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3513
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3382 - val_loss: 0.3523
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3380 - val_loss: 0.3521
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3538
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3392 - val_loss: 0.3509
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3536
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3388 - val_loss: 0.3518
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3380 - val_loss: 0.3535
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3388 - val_loss: 0.3553
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3385 - val_loss: 0.3504
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3379 - val_loss: 0.3522
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3376 - val_loss: 0.3531
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3529
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3520
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3377 - val_loss: 0.3514
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3383 - val_loss: 0.3517
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3501
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3384 - val_loss: 0.3520
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3530
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3353 - val_loss: 0.3524
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_340 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1120 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1120 [()] 0 tf_op_layer_Shape_1120[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_112 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1120[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_112[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_336 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_112 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_336[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1121 (TensorF [(2,)] 0 tf_op_layer_Add_112[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1123 (TensorF [(2,)] 0 tf_op_layer_Add_112[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1122 (TensorF [(2,)] 0 tf_op_layer_Add_112[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1124 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1126 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1125 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1127 (TensorF [(2,)] 0 tf_op_layer_Add_112[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1129 (TensorF [(2,)] 0 tf_op_layer_Add_112[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1128 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1121 [()] 0 tf_op_layer_Shape_1121[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1123 [()] 0 tf_op_layer_Shape_1123[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1122 [()] 0 tf_op_layer_Shape_1122[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1124 [()] 0 tf_op_layer_Shape_1124[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1126 [()] 0 tf_op_layer_Shape_1126[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1125 [()] 0 tf_op_layer_Shape_1125[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1127 [()] 0 tf_op_layer_Shape_1127[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1129 [()] 0 tf_op_layer_Shape_1129[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1128 [()] 0 tf_op_layer_Shape_1128[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_672/shape ( [(3,)] 0 tf_op_layer_strided_slice_1121[0]
tf_op_layer_strided_slice_1123[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_673/shape ( [(3,)] 0 tf_op_layer_strided_slice_1122[0]
tf_op_layer_strided_slice_1123[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_674/shape ( [(3,)] 0 tf_op_layer_strided_slice_1124[0]
tf_op_layer_strided_slice_1126[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_675/shape ( [(3,)] 0 tf_op_layer_strided_slice_1125[0]
tf_op_layer_strided_slice_1126[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_676/shape ( [(3,)] 0 tf_op_layer_strided_slice_1127[0]
tf_op_layer_strided_slice_1129[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_677/shape ( [(3,)] 0 tf_op_layer_strided_slice_1128[0]
tf_op_layer_strided_slice_1129[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_672 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_112[0][0]
tf_op_layer_Reshape_672/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_672/multiples [(3,)] 0 tf_op_layer_strided_slice_1122[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_673 (Tensor [(1, None, None)] 0 tf_op_layer_Add_112[0][0]
tf_op_layer_Reshape_673/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_673/multiples [(3,)] 0 tf_op_layer_strided_slice_1121[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_674 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_674/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_674/multiples [(3,)] 0 tf_op_layer_strided_slice_1125[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_675 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_675/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_675/multiples [(3,)] 0 tf_op_layer_strided_slice_1124[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_676 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_112[0][0]
tf_op_layer_Reshape_676/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_676/multiples [(3,)] 0 tf_op_layer_strided_slice_1128[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_677 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_677/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_677/multiples [(3,)] 0 tf_op_layer_strided_slice_1127[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_672 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_672[0][0]
tf_op_layer_Tile_672/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_673 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_673[0][0]
tf_op_layer_Tile_673/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_674 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_674[0][0]
tf_op_layer_Tile_674/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_675 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_675[0][0]
tf_op_layer_Tile_675/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_676 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_676[0][0]
tf_op_layer_Tile_676/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_677 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_677[0][0]
tf_op_layer_Tile_677/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_560 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_672[0][0]
tf_op_layer_Tile_673[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_561 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_674[0][0]
tf_op_layer_Tile_675[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_562 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_676[0][0]
tf_op_layer_Tile_677[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_448 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_560[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_449 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_561[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_450 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_562[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_784 (TensorFlo [(None, None)] 0 tf_op_layer_Square_448[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_785 (TensorFlo [(None, None)] 0 tf_op_layer_Square_449[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_786 (TensorFlo [(None, None)] 0 tf_op_layer_Square_450[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_336 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_784[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_336 (TensorFlo [()] 0 tf_op_layer_strided_slice_1123[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_337 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_785[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_337 (TensorFlo [()] 0 tf_op_layer_strided_slice_1126[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_338 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_786[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_338 (TensorFlo [()] 0 tf_op_layer_strided_slice_1129[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_336 (Tensor [(None, None)] 0 tf_op_layer_Neg_336[0][0]
tf_op_layer_Cast_336[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_337 (Tensor [(None, None)] 0 tf_op_layer_Neg_337[0][0]
tf_op_layer_Cast_337[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_338 (Tensor [(None, None)] 0 tf_op_layer_Neg_338[0][0]
tf_op_layer_Cast_338[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_336 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_336[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_337 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_337[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_338 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_338[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_787 (TensorFlo [()] 0 tf_op_layer_Exp_336[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_788 (TensorFlo [()] 0 tf_op_layer_Exp_337[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_789 (TensorFlo [()] 0 tf_op_layer_Exp_338[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_564 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_224 (TensorFl [()] 0 tf_op_layer_Mean_787[0][0]
tf_op_layer_Mean_788[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_337 (TensorFlow [()] 0 tf_op_layer_Mean_789[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_451 (TensorF [(None, 97)] 0 tf_op_layer_Sub_564[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_563 (TensorFlow [()] 0 tf_op_layer_AddV2_224[0][0]
tf_op_layer_Mul_337[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_112 (TensorFlow [(None,)] 0 tf_op_layer_Square_451[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_338 (TensorFlow [()] 0 tf_op_layer_Sub_563[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_225 (TensorFl [(None,)] 0 tf_op_layer_Sum_112[0][0]
tf_op_layer_Mul_338[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_790 (TensorFlo [()] 0 tf_op_layer_AddV2_225[0][0]
__________________________________________________________________________________________________
add_loss_112 (AddLoss) () 0 tf_op_layer_Mean_790[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.7883WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.0925s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9501 - val_loss: 0.9911
Epoch 2/100
218/218 [==============================] - 0s 928us/step - loss: 0.8914 - val_loss: 0.8086
Epoch 3/100
218/218 [==============================] - 0s 933us/step - loss: 0.7668 - val_loss: 0.7178
Epoch 4/100
218/218 [==============================] - 0s 930us/step - loss: 0.6886 - val_loss: 0.6540
Epoch 5/100
218/218 [==============================] - 0s 930us/step - loss: 0.6331 - val_loss: 0.6037
Epoch 6/100
218/218 [==============================] - 0s 934us/step - loss: 0.6010 - val_loss: 0.5775
Epoch 7/100
218/218 [==============================] - 0s 925us/step - loss: 0.5769 - val_loss: 0.5668
Epoch 8/100
218/218 [==============================] - 0s 925us/step - loss: 0.5632 - val_loss: 0.5546
Epoch 9/100
218/218 [==============================] - 0s 932us/step - loss: 0.5552 - val_loss: 0.5434
Epoch 10/100
218/218 [==============================] - 0s 941us/step - loss: 0.5430 - val_loss: 0.5293
Epoch 11/100
218/218 [==============================] - 0s 934us/step - loss: 0.5349 - val_loss: 0.5315
Epoch 12/100
218/218 [==============================] - 0s 934us/step - loss: 0.5283 - val_loss: 0.5305
Epoch 13/100
218/218 [==============================] - 0s 930us/step - loss: 0.5237 - val_loss: 0.5258
Epoch 14/100
218/218 [==============================] - 0s 932us/step - loss: 0.5206 - val_loss: 0.5150
Epoch 15/100
218/218 [==============================] - 0s 928us/step - loss: 0.5169 - val_loss: 0.5111
Epoch 16/100
218/218 [==============================] - 0s 933us/step - loss: 0.5120 - val_loss: 0.5102
Epoch 17/100
218/218 [==============================] - 0s 930us/step - loss: 0.5086 - val_loss: 0.5000
Epoch 18/100
218/218 [==============================] - 0s 924us/step - loss: 0.5070 - val_loss: 0.5043
Epoch 19/100
218/218 [==============================] - 0s 927us/step - loss: 0.5033 - val_loss: 0.4993
Epoch 20/100
218/218 [==============================] - 0s 928us/step - loss: 0.5013 - val_loss: 0.4961
Epoch 21/100
218/218 [==============================] - 0s 934us/step - loss: 0.4983 - val_loss: 0.4971
Epoch 22/100
218/218 [==============================] - 0s 927us/step - loss: 0.4968 - val_loss: 0.4926
Epoch 23/100
218/218 [==============================] - 0s 929us/step - loss: 0.4951 - val_loss: 0.4893
Epoch 24/100
218/218 [==============================] - 0s 938us/step - loss: 0.4909 - val_loss: 0.4909
Epoch 25/100
218/218 [==============================] - 0s 930us/step - loss: 0.4921 - val_loss: 0.4879
Epoch 26/100
218/218 [==============================] - 0s 938us/step - loss: 0.4881 - val_loss: 0.4873
Epoch 27/100
218/218 [==============================] - 0s 930us/step - loss: 0.4884 - val_loss: 0.4905
Epoch 28/100
218/218 [==============================] - 0s 934us/step - loss: 0.4832 - val_loss: 0.4880
Epoch 29/100
218/218 [==============================] - 0s 925us/step - loss: 0.4880 - val_loss: 0.4798
Epoch 30/100
218/218 [==============================] - 0s 926us/step - loss: 0.4819 - val_loss: 0.4850
Epoch 31/100
218/218 [==============================] - 0s 934us/step - loss: 0.4829 - val_loss: 0.4802
Epoch 32/100
218/218 [==============================] - 0s 932us/step - loss: 0.4853 - val_loss: 0.4784
Epoch 33/100
218/218 [==============================] - 0s 924us/step - loss: 0.4798 - val_loss: 0.4764
Epoch 34/100
218/218 [==============================] - 0s 923us/step - loss: 0.4815 - val_loss: 0.4811
Epoch 35/100
218/218 [==============================] - 0s 933us/step - loss: 0.4813 - val_loss: 0.4812
Epoch 36/100
218/218 [==============================] - 0s 927us/step - loss: 0.4807 - val_loss: 0.4799
Epoch 37/100
218/218 [==============================] - 0s 922us/step - loss: 0.4777 - val_loss: 0.4804
Epoch 38/100
218/218 [==============================] - 0s 934us/step - loss: 0.4761 - val_loss: 0.4758
Epoch 39/100
218/218 [==============================] - 0s 928us/step - loss: 0.4762 - val_loss: 0.4712
Epoch 40/100
218/218 [==============================] - 0s 927us/step - loss: 0.4769 - val_loss: 0.4739
Epoch 41/100
218/218 [==============================] - 0s 927us/step - loss: 0.4763 - val_loss: 0.4726
Epoch 42/100
218/218 [==============================] - 0s 924us/step - loss: 0.4745 - val_loss: 0.4764
Epoch 43/100
218/218 [==============================] - 0s 926us/step - loss: 0.4733 - val_loss: 0.4700
Epoch 44/100
218/218 [==============================] - 0s 926us/step - loss: 0.4741 - val_loss: 0.4725
Epoch 45/100
218/218 [==============================] - 0s 929us/step - loss: 0.4725 - val_loss: 0.4708
Epoch 46/100
218/218 [==============================] - 0s 922us/step - loss: 0.4724 - val_loss: 0.4774
Epoch 47/100
218/218 [==============================] - 0s 936us/step - loss: 0.4721 - val_loss: 0.4714
Epoch 48/100
218/218 [==============================] - 0s 940us/step - loss: 0.4704 - val_loss: 0.4674
Epoch 49/100
218/218 [==============================] - 0s 933us/step - loss: 0.4718 - val_loss: 0.4672
Epoch 50/100
218/218 [==============================] - 0s 948us/step - loss: 0.4723 - val_loss: 0.4661
Epoch 51/100
218/218 [==============================] - 0s 940us/step - loss: 0.4707 - val_loss: 0.4670
Epoch 52/100
218/218 [==============================] - 0s 933us/step - loss: 0.4700 - val_loss: 0.4652
Epoch 53/100
218/218 [==============================] - 0s 933us/step - loss: 0.4701 - val_loss: 0.4669
Epoch 54/100
218/218 [==============================] - 0s 936us/step - loss: 0.4693 - val_loss: 0.4705
Epoch 55/100
218/218 [==============================] - 0s 942us/step - loss: 0.4694 - val_loss: 0.4689
Epoch 56/100
218/218 [==============================] - 0s 942us/step - loss: 0.4711 - val_loss: 0.4667
Epoch 57/100
218/218 [==============================] - 0s 938us/step - loss: 0.4701 - val_loss: 0.4644
Epoch 58/100
218/218 [==============================] - 0s 939us/step - loss: 0.4684 - val_loss: 0.4638
Epoch 59/100
218/218 [==============================] - 0s 906us/step - loss: 0.4659 - val_loss: 0.4684
Epoch 60/100
218/218 [==============================] - 0s 954us/step - loss: 0.4676 - val_loss: 0.4669
Epoch 61/100
218/218 [==============================] - 0s 938us/step - loss: 0.4674 - val_loss: 0.4652
Epoch 62/100
218/218 [==============================] - 0s 935us/step - loss: 0.4680 - val_loss: 0.4652
Epoch 63/100
218/218 [==============================] - 0s 933us/step - loss: 0.4661 - val_loss: 0.4657
Epoch 64/100
218/218 [==============================] - 0s 939us/step - loss: 0.4635 - val_loss: 0.4656
Epoch 65/100
218/218 [==============================] - 0s 933us/step - loss: 0.4665 - val_loss: 0.4619
Epoch 66/100
218/218 [==============================] - 0s 941us/step - loss: 0.4657 - val_loss: 0.4668
Epoch 67/100
218/218 [==============================] - 0s 940us/step - loss: 0.4652 - val_loss: 0.4665
Epoch 68/100
218/218 [==============================] - 0s 937us/step - loss: 0.4654 - val_loss: 0.4626
Epoch 69/100
218/218 [==============================] - 0s 947us/step - loss: 0.4668 - val_loss: 0.4615
Epoch 70/100
218/218 [==============================] - 0s 943us/step - loss: 0.4655 - val_loss: 0.4623
Epoch 71/100
218/218 [==============================] - 0s 934us/step - loss: 0.4634 - val_loss: 0.4649
Epoch 72/100
218/218 [==============================] - 0s 949us/step - loss: 0.4635 - val_loss: 0.4639
Epoch 73/100
218/218 [==============================] - 0s 945us/step - loss: 0.4632 - val_loss: 0.4630
Epoch 74/100
218/218 [==============================] - 0s 936us/step - loss: 0.4637 - val_loss: 0.4660
Epoch 75/100
218/218 [==============================] - 0s 943us/step - loss: 0.4651 - val_loss: 0.4622
Epoch 76/100
218/218 [==============================] - 0s 949us/step - loss: 0.4636 - val_loss: 0.4658
Epoch 77/100
218/218 [==============================] - 0s 971us/step - loss: 0.4628 - val_loss: 0.4600
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4635 - val_loss: 0.4614
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4631 - val_loss: 0.4633
Epoch 80/100
218/218 [==============================] - 0s 985us/step - loss: 0.4619 - val_loss: 0.4593
Epoch 81/100
218/218 [==============================] - 0s 985us/step - loss: 0.4628 - val_loss: 0.4592
Epoch 82/100
218/218 [==============================] - 0s 980us/step - loss: 0.4634 - val_loss: 0.4642
Epoch 83/100
218/218 [==============================] - 0s 972us/step - loss: 0.4615 - val_loss: 0.4638
Epoch 84/100
218/218 [==============================] - 0s 975us/step - loss: 0.4625 - val_loss: 0.4620
Epoch 85/100
218/218 [==============================] - 0s 963us/step - loss: 0.4617 - val_loss: 0.4598
Epoch 86/100
218/218 [==============================] - 0s 968us/step - loss: 0.4614 - val_loss: 0.4599
Epoch 87/100
218/218 [==============================] - 0s 968us/step - loss: 0.4615 - val_loss: 0.4604
Epoch 88/100
218/218 [==============================] - 0s 972us/step - loss: 0.4626 - val_loss: 0.4622
Epoch 89/100
218/218 [==============================] - 0s 964us/step - loss: 0.4618 - val_loss: 0.4624
Epoch 90/100
218/218 [==============================] - 0s 976us/step - loss: 0.4612 - val_loss: 0.4660
Epoch 91/100
218/218 [==============================] - 0s 979us/step - loss: 0.4621 - val_loss: 0.4591
Epoch 92/100
218/218 [==============================] - 0s 974us/step - loss: 0.4609 - val_loss: 0.4644
Epoch 93/100
218/218 [==============================] - 0s 976us/step - loss: 0.4611 - val_loss: 0.4595
Epoch 94/100
218/218 [==============================] - 0s 965us/step - loss: 0.4605 - val_loss: 0.4605
Epoch 95/100
218/218 [==============================] - 0s 986us/step - loss: 0.4616 - val_loss: 0.4580
Epoch 96/100
218/218 [==============================] - 0s 975us/step - loss: 0.4592 - val_loss: 0.4560
Epoch 97/100
218/218 [==============================] - 0s 964us/step - loss: 0.4589 - val_loss: 0.4587
Epoch 98/100
218/218 [==============================] - 0s 993us/step - loss: 0.4602 - val_loss: 0.4590
Epoch 99/100
218/218 [==============================] - 0s 980us/step - loss: 0.4585 - val_loss: 0.4592
Epoch 100/100
218/218 [==============================] - 0s 971us/step - loss: 0.4603 - val_loss: 0.4597
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.20 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.46 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.86 seconds.
Calculating KMeans...
Calculated KMeans in 14.20 seconds.
Calculated landmark operator in 16.53 seconds.
Calculating optimal t...
Automatically selected t = 34
Calculated optimal t in 2.91 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.53 seconds.
Calculating metric MDS...
Calculated metric MDS in 17.06 seconds.
Calculated PHATE in 50.51 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_341 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_342 (Dense) (None, 32) 2080 dense_341[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_342[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_342[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_343 (Dense) (None, 32) 128
_________________________________________________________________
dense_344 (Dense) (None, 64) 2112
_________________________________________________________________
dense_345 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_341 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_342 (Dense) (None, 32) 2080 dense_341[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_342[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_342[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1130 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1130 [()] 0 tf_op_layer_Shape_1130[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_113 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1130[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_113[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_339 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_113 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_339[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1131 (TensorF [(2,)] 0 tf_op_layer_Add_113[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1133 (TensorF [(2,)] 0 tf_op_layer_Add_113[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1132 (TensorF [(2,)] 0 tf_op_layer_Add_113[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1134 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1136 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1135 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1137 (TensorF [(2,)] 0 tf_op_layer_Add_113[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1139 (TensorF [(2,)] 0 tf_op_layer_Add_113[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1138 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1131 [()] 0 tf_op_layer_Shape_1131[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1133 [()] 0 tf_op_layer_Shape_1133[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1132 [()] 0 tf_op_layer_Shape_1132[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1134 [()] 0 tf_op_layer_Shape_1134[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1136 [()] 0 tf_op_layer_Shape_1136[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1135 [()] 0 tf_op_layer_Shape_1135[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1137 [()] 0 tf_op_layer_Shape_1137[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1139 [()] 0 tf_op_layer_Shape_1139[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1138 [()] 0 tf_op_layer_Shape_1138[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_678/shape ( [(3,)] 0 tf_op_layer_strided_slice_1131[0]
tf_op_layer_strided_slice_1133[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_679/shape ( [(3,)] 0 tf_op_layer_strided_slice_1132[0]
tf_op_layer_strided_slice_1133[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_680/shape ( [(3,)] 0 tf_op_layer_strided_slice_1134[0]
tf_op_layer_strided_slice_1136[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_681/shape ( [(3,)] 0 tf_op_layer_strided_slice_1135[0]
tf_op_layer_strided_slice_1136[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_682/shape ( [(3,)] 0 tf_op_layer_strided_slice_1137[0]
tf_op_layer_strided_slice_1139[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_683/shape ( [(3,)] 0 tf_op_layer_strided_slice_1138[0]
tf_op_layer_strided_slice_1139[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_678 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_113[0][0]
tf_op_layer_Reshape_678/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_678/multiples [(3,)] 0 tf_op_layer_strided_slice_1132[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_679 (Tensor [(1, None, None)] 0 tf_op_layer_Add_113[0][0]
tf_op_layer_Reshape_679/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_679/multiples [(3,)] 0 tf_op_layer_strided_slice_1131[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_680 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_680/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_680/multiples [(3,)] 0 tf_op_layer_strided_slice_1135[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_681 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_681/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_681/multiples [(3,)] 0 tf_op_layer_strided_slice_1134[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_682 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_113[0][0]
tf_op_layer_Reshape_682/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_682/multiples [(3,)] 0 tf_op_layer_strided_slice_1138[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_683 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_683/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_683/multiples [(3,)] 0 tf_op_layer_strided_slice_1137[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_678 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_678[0][0]
tf_op_layer_Tile_678/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_679 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_679[0][0]
tf_op_layer_Tile_679/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_680 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_680[0][0]
tf_op_layer_Tile_680/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_681 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_681[0][0]
tf_op_layer_Tile_681/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_682 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_682[0][0]
tf_op_layer_Tile_682/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_683 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_683[0][0]
tf_op_layer_Tile_683/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_565 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_678[0][0]
tf_op_layer_Tile_679[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_566 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_680[0][0]
tf_op_layer_Tile_681[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_567 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_682[0][0]
tf_op_layer_Tile_683[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_452 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_565[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_453 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_566[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_454 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_567[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_791 (TensorFlo [(None, None)] 0 tf_op_layer_Square_452[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_792 (TensorFlo [(None, None)] 0 tf_op_layer_Square_453[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_793 (TensorFlo [(None, None)] 0 tf_op_layer_Square_454[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_339 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_791[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_339 (TensorFlo [()] 0 tf_op_layer_strided_slice_1133[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_340 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_792[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_340 (TensorFlo [()] 0 tf_op_layer_strided_slice_1136[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_341 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_793[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_341 (TensorFlo [()] 0 tf_op_layer_strided_slice_1139[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_339 (Tensor [(None, None)] 0 tf_op_layer_Neg_339[0][0]
tf_op_layer_Cast_339[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_340 (Tensor [(None, None)] 0 tf_op_layer_Neg_340[0][0]
tf_op_layer_Cast_340[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_341 (Tensor [(None, None)] 0 tf_op_layer_Neg_341[0][0]
tf_op_layer_Cast_341[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_339 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_339[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_340 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_340[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_341 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_341[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_794 (TensorFlo [()] 0 tf_op_layer_Exp_339[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_795 (TensorFlo [()] 0 tf_op_layer_Exp_340[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_796 (TensorFlo [()] 0 tf_op_layer_Exp_341[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_569 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_226 (TensorFl [()] 0 tf_op_layer_Mean_794[0][0]
tf_op_layer_Mean_795[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_340 (TensorFlow [()] 0 tf_op_layer_Mean_796[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_455 (TensorF [(None, 97)] 0 tf_op_layer_Sub_569[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_568 (TensorFlow [()] 0 tf_op_layer_AddV2_226[0][0]
tf_op_layer_Mul_340[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_113 (TensorFlow [(None,)] 0 tf_op_layer_Square_455[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_341 (TensorFlow [()] 0 tf_op_layer_Sub_568[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_227 (TensorFl [(None,)] 0 tf_op_layer_Sum_113[0][0]
tf_op_layer_Mul_341[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_797 (TensorFlo [()] 0 tf_op_layer_AddV2_227[0][0]
__________________________________________________________________________________________________
add_loss_113 (AddLoss) () 0 tf_op_layer_Mean_797[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 9s - loss: 7.7241WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.0898s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 0.9667 - val_loss: 0.4827
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4221 - val_loss: 0.4056
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3859 - val_loss: 0.3921
Epoch 4/100
218/218 [==============================] - 0s 1000us/step - loss: 0.3765 - val_loss: 0.3869
Epoch 5/100
218/218 [==============================] - 0s 991us/step - loss: 0.3705 - val_loss: 0.3825
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3682 - val_loss: 0.3792
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3642 - val_loss: 0.3761
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3619 - val_loss: 0.3706
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3599 - val_loss: 0.3763
Epoch 10/100
218/218 [==============================] - 0s 997us/step - loss: 0.3593 - val_loss: 0.3687
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3568 - val_loss: 0.3675
Epoch 12/100
218/218 [==============================] - 0s 996us/step - loss: 0.3549 - val_loss: 0.3689
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3681
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3527 - val_loss: 0.3644
Epoch 15/100
218/218 [==============================] - 0s 936us/step - loss: 0.3529 - val_loss: 0.3658
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3532 - val_loss: 0.3641
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3529 - val_loss: 0.3647
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3511 - val_loss: 0.3648
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3514 - val_loss: 0.3648
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3495 - val_loss: 0.3615
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3497 - val_loss: 0.3612
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3637
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3607
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3662
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3637
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3621
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3606
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3606
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3594
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3596
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3605
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3618
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3586
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3597
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3625
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3596
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3580
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3588
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3569
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3557
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3592
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3587
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3586
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3599
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3579
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3585
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3567
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3583
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3586
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3566
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3565
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3566
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3605
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3574
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3563
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3529
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3559
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3591
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3583
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3574
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3542
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3405 - val_loss: 0.3575
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3568
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3569
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3560
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3555
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3557
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3550
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3555
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3565
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3545
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3400 - val_loss: 0.3522
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3552
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3405 - val_loss: 0.3543
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3540
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3569
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3405 - val_loss: 0.3530
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3563
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3542
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3526
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3534
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3534
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3532
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3388 - val_loss: 0.3520
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3540
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3400 - val_loss: 0.3539
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3515
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3575
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3496
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3385 - val_loss: 0.3540
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3385 - val_loss: 0.3534
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3387 - val_loss: 0.3524
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3545
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3369 - val_loss: 0.3526
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3380 - val_loss: 0.3526
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3380 - val_loss: 0.3492
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3368 - val_loss: 0.3520
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3366 - val_loss: 0.3522
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3383 - val_loss: 0.3538
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3368 - val_loss: 0.3532
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_346 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1140 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1140 [()] 0 tf_op_layer_Shape_1140[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_114 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1140[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_114[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_342 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_114 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_342[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1141 (TensorF [(2,)] 0 tf_op_layer_Add_114[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1143 (TensorF [(2,)] 0 tf_op_layer_Add_114[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1142 (TensorF [(2,)] 0 tf_op_layer_Add_114[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1144 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1146 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1145 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1147 (TensorF [(2,)] 0 tf_op_layer_Add_114[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1149 (TensorF [(2,)] 0 tf_op_layer_Add_114[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1148 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1141 [()] 0 tf_op_layer_Shape_1141[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1143 [()] 0 tf_op_layer_Shape_1143[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1142 [()] 0 tf_op_layer_Shape_1142[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1144 [()] 0 tf_op_layer_Shape_1144[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1146 [()] 0 tf_op_layer_Shape_1146[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1145 [()] 0 tf_op_layer_Shape_1145[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1147 [()] 0 tf_op_layer_Shape_1147[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1149 [()] 0 tf_op_layer_Shape_1149[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1148 [()] 0 tf_op_layer_Shape_1148[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_684/shape ( [(3,)] 0 tf_op_layer_strided_slice_1141[0]
tf_op_layer_strided_slice_1143[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_685/shape ( [(3,)] 0 tf_op_layer_strided_slice_1142[0]
tf_op_layer_strided_slice_1143[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_686/shape ( [(3,)] 0 tf_op_layer_strided_slice_1144[0]
tf_op_layer_strided_slice_1146[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_687/shape ( [(3,)] 0 tf_op_layer_strided_slice_1145[0]
tf_op_layer_strided_slice_1146[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_688/shape ( [(3,)] 0 tf_op_layer_strided_slice_1147[0]
tf_op_layer_strided_slice_1149[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_689/shape ( [(3,)] 0 tf_op_layer_strided_slice_1148[0]
tf_op_layer_strided_slice_1149[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_684 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_114[0][0]
tf_op_layer_Reshape_684/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_684/multiples [(3,)] 0 tf_op_layer_strided_slice_1142[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_685 (Tensor [(1, None, None)] 0 tf_op_layer_Add_114[0][0]
tf_op_layer_Reshape_685/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_685/multiples [(3,)] 0 tf_op_layer_strided_slice_1141[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_686 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_686/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_686/multiples [(3,)] 0 tf_op_layer_strided_slice_1145[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_687 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_687/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_687/multiples [(3,)] 0 tf_op_layer_strided_slice_1144[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_688 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_114[0][0]
tf_op_layer_Reshape_688/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_688/multiples [(3,)] 0 tf_op_layer_strided_slice_1148[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_689 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_689/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_689/multiples [(3,)] 0 tf_op_layer_strided_slice_1147[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_684 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_684[0][0]
tf_op_layer_Tile_684/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_685 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_685[0][0]
tf_op_layer_Tile_685/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_686 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_686[0][0]
tf_op_layer_Tile_686/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_687 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_687[0][0]
tf_op_layer_Tile_687/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_688 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_688[0][0]
tf_op_layer_Tile_688/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_689 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_689[0][0]
tf_op_layer_Tile_689/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_570 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_684[0][0]
tf_op_layer_Tile_685[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_571 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_686[0][0]
tf_op_layer_Tile_687[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_572 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_688[0][0]
tf_op_layer_Tile_689[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_456 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_570[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_457 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_571[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_458 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_572[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_798 (TensorFlo [(None, None)] 0 tf_op_layer_Square_456[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_799 (TensorFlo [(None, None)] 0 tf_op_layer_Square_457[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_800 (TensorFlo [(None, None)] 0 tf_op_layer_Square_458[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_342 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_798[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_342 (TensorFlo [()] 0 tf_op_layer_strided_slice_1143[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_343 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_799[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_343 (TensorFlo [()] 0 tf_op_layer_strided_slice_1146[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_344 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_800[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_344 (TensorFlo [()] 0 tf_op_layer_strided_slice_1149[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_342 (Tensor [(None, None)] 0 tf_op_layer_Neg_342[0][0]
tf_op_layer_Cast_342[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_343 (Tensor [(None, None)] 0 tf_op_layer_Neg_343[0][0]
tf_op_layer_Cast_343[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_344 (Tensor [(None, None)] 0 tf_op_layer_Neg_344[0][0]
tf_op_layer_Cast_344[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_342 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_342[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_343 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_343[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_344 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_344[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_801 (TensorFlo [()] 0 tf_op_layer_Exp_342[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_802 (TensorFlo [()] 0 tf_op_layer_Exp_343[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_803 (TensorFlo [()] 0 tf_op_layer_Exp_344[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_574 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_228 (TensorFl [()] 0 tf_op_layer_Mean_801[0][0]
tf_op_layer_Mean_802[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_343 (TensorFlow [()] 0 tf_op_layer_Mean_803[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_459 (TensorF [(None, 97)] 0 tf_op_layer_Sub_574[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_573 (TensorFlow [()] 0 tf_op_layer_AddV2_228[0][0]
tf_op_layer_Mul_343[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_114 (TensorFlow [(None,)] 0 tf_op_layer_Square_459[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_344 (TensorFlow [()] 0 tf_op_layer_Sub_573[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_229 (TensorFl [(None,)] 0 tf_op_layer_Sum_114[0][0]
tf_op_layer_Mul_344[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_804 (TensorFlo [()] 0 tf_op_layer_AddV2_229[0][0]
__________________________________________________________________________________________________
add_loss_114 (AddLoss) () 0 tf_op_layer_Mean_804[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 7.8499WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.1141s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9908 - val_loss: 0.9668
Epoch 2/100
218/218 [==============================] - 0s 926us/step - loss: 0.8377 - val_loss: 0.7584
Epoch 3/100
218/218 [==============================] - 0s 946us/step - loss: 0.7019 - val_loss: 0.6726
Epoch 4/100
218/218 [==============================] - 0s 924us/step - loss: 0.6449 - val_loss: 0.6324
Epoch 5/100
218/218 [==============================] - 0s 930us/step - loss: 0.6060 - val_loss: 0.6073
Epoch 6/100
218/218 [==============================] - 0s 923us/step - loss: 0.5846 - val_loss: 0.5806
Epoch 7/100
218/218 [==============================] - 0s 923us/step - loss: 0.5691 - val_loss: 0.5819
Epoch 8/100
218/218 [==============================] - 0s 939us/step - loss: 0.5586 - val_loss: 0.5647
Epoch 9/100
218/218 [==============================] - 0s 929us/step - loss: 0.5469 - val_loss: 0.5583
Epoch 10/100
218/218 [==============================] - 0s 930us/step - loss: 0.5409 - val_loss: 0.5523
Epoch 11/100
218/218 [==============================] - 0s 924us/step - loss: 0.5358 - val_loss: 0.5451
Epoch 12/100
218/218 [==============================] - 0s 925us/step - loss: 0.5293 - val_loss: 0.5345
Epoch 13/100
218/218 [==============================] - 0s 925us/step - loss: 0.5263 - val_loss: 0.5368
Epoch 14/100
218/218 [==============================] - 0s 925us/step - loss: 0.5183 - val_loss: 0.5288
Epoch 15/100
218/218 [==============================] - 0s 930us/step - loss: 0.5167 - val_loss: 0.5282
Epoch 16/100
218/218 [==============================] - 0s 921us/step - loss: 0.5119 - val_loss: 0.5261
Epoch 17/100
218/218 [==============================] - 0s 918us/step - loss: 0.5117 - val_loss: 0.5249
Epoch 18/100
218/218 [==============================] - 0s 930us/step - loss: 0.5066 - val_loss: 0.5150
Epoch 19/100
218/218 [==============================] - 0s 928us/step - loss: 0.5023 - val_loss: 0.5151
Epoch 20/100
218/218 [==============================] - 0s 918us/step - loss: 0.5053 - val_loss: 0.5172
Epoch 21/100
218/218 [==============================] - 0s 921us/step - loss: 0.5010 - val_loss: 0.5106
Epoch 22/100
218/218 [==============================] - 0s 929us/step - loss: 0.5008 - val_loss: 0.5087
Epoch 23/100
218/218 [==============================] - 0s 925us/step - loss: 0.4995 - val_loss: 0.5116
Epoch 24/100
218/218 [==============================] - 0s 920us/step - loss: 0.4962 - val_loss: 0.5036
Epoch 25/100
218/218 [==============================] - 0s 920us/step - loss: 0.4941 - val_loss: 0.5058
Epoch 26/100
218/218 [==============================] - 0s 918us/step - loss: 0.4911 - val_loss: 0.5017
Epoch 27/100
218/218 [==============================] - 0s 924us/step - loss: 0.4929 - val_loss: 0.5017
Epoch 28/100
218/218 [==============================] - 0s 925us/step - loss: 0.4909 - val_loss: 0.5049
Epoch 29/100
218/218 [==============================] - 0s 919us/step - loss: 0.4872 - val_loss: 0.5007
Epoch 30/100
218/218 [==============================] - 0s 926us/step - loss: 0.4860 - val_loss: 0.4987
Epoch 31/100
218/218 [==============================] - 0s 923us/step - loss: 0.4863 - val_loss: 0.4992
Epoch 32/100
218/218 [==============================] - 0s 937us/step - loss: 0.4863 - val_loss: 0.5019
Epoch 33/100
218/218 [==============================] - 0s 929us/step - loss: 0.4868 - val_loss: 0.5011
Epoch 34/100
218/218 [==============================] - 0s 917us/step - loss: 0.4845 - val_loss: 0.5006
Epoch 35/100
218/218 [==============================] - 0s 913us/step - loss: 0.4846 - val_loss: 0.4942
Epoch 36/100
218/218 [==============================] - 0s 911us/step - loss: 0.4819 - val_loss: 0.4938
Epoch 37/100
218/218 [==============================] - 0s 923us/step - loss: 0.4817 - val_loss: 0.4925
Epoch 38/100
218/218 [==============================] - 0s 930us/step - loss: 0.4777 - val_loss: 0.4954
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4806 - val_loss: 0.4973
Epoch 40/100
218/218 [==============================] - 0s 939us/step - loss: 0.4781 - val_loss: 0.4916
Epoch 41/100
218/218 [==============================] - 0s 949us/step - loss: 0.4765 - val_loss: 0.4881
Epoch 42/100
218/218 [==============================] - 0s 958us/step - loss: 0.4772 - val_loss: 0.4920
Epoch 43/100
218/218 [==============================] - 0s 934us/step - loss: 0.4769 - val_loss: 0.4889
Epoch 44/100
218/218 [==============================] - 0s 922us/step - loss: 0.4756 - val_loss: 0.4881
Epoch 45/100
218/218 [==============================] - 0s 920us/step - loss: 0.4757 - val_loss: 0.4904
Epoch 46/100
218/218 [==============================] - 0s 911us/step - loss: 0.4755 - val_loss: 0.4880
Epoch 47/100
218/218 [==============================] - 0s 934us/step - loss: 0.4733 - val_loss: 0.4862
Epoch 48/100
218/218 [==============================] - 0s 923us/step - loss: 0.4746 - val_loss: 0.4850
Epoch 49/100
218/218 [==============================] - 0s 959us/step - loss: 0.4762 - val_loss: 0.4858
Epoch 50/100
218/218 [==============================] - 0s 933us/step - loss: 0.4729 - val_loss: 0.4862
Epoch 51/100
218/218 [==============================] - 0s 933us/step - loss: 0.4752 - val_loss: 0.4881
Epoch 52/100
218/218 [==============================] - 0s 933us/step - loss: 0.4715 - val_loss: 0.4808
Epoch 53/100
218/218 [==============================] - 0s 950us/step - loss: 0.4733 - val_loss: 0.4882
Epoch 54/100
218/218 [==============================] - 0s 929us/step - loss: 0.4713 - val_loss: 0.4852
Epoch 55/100
218/218 [==============================] - 0s 953us/step - loss: 0.4712 - val_loss: 0.4876
Epoch 56/100
218/218 [==============================] - 0s 933us/step - loss: 0.4719 - val_loss: 0.4852
Epoch 57/100
218/218 [==============================] - 0s 924us/step - loss: 0.4704 - val_loss: 0.4816
Epoch 58/100
218/218 [==============================] - 0s 936us/step - loss: 0.4695 - val_loss: 0.4841
Epoch 59/100
218/218 [==============================] - 0s 931us/step - loss: 0.4682 - val_loss: 0.4821
Epoch 60/100
218/218 [==============================] - 0s 965us/step - loss: 0.4684 - val_loss: 0.4821
Epoch 61/100
218/218 [==============================] - 0s 934us/step - loss: 0.4683 - val_loss: 0.4835
Epoch 62/100
218/218 [==============================] - 0s 941us/step - loss: 0.4680 - val_loss: 0.4844
Epoch 63/100
218/218 [==============================] - 0s 940us/step - loss: 0.4688 - val_loss: 0.4853
Epoch 64/100
218/218 [==============================] - 0s 931us/step - loss: 0.4673 - val_loss: 0.4844
Epoch 65/100
218/218 [==============================] - 0s 932us/step - loss: 0.4659 - val_loss: 0.4819
Epoch 66/100
218/218 [==============================] - 0s 912us/step - loss: 0.4677 - val_loss: 0.4815
Epoch 67/100
218/218 [==============================] - 0s 922us/step - loss: 0.4665 - val_loss: 0.4865
Epoch 68/100
218/218 [==============================] - 0s 924us/step - loss: 0.4676 - val_loss: 0.4797
Epoch 69/100
218/218 [==============================] - 0s 900us/step - loss: 0.4678 - val_loss: 0.4810
Epoch 70/100
218/218 [==============================] - 0s 934us/step - loss: 0.4687 - val_loss: 0.4799
Epoch 71/100
218/218 [==============================] - 0s 930us/step - loss: 0.4667 - val_loss: 0.4794
Epoch 72/100
218/218 [==============================] - 0s 932us/step - loss: 0.4671 - val_loss: 0.4823
Epoch 73/100
218/218 [==============================] - 0s 930us/step - loss: 0.4680 - val_loss: 0.4784
Epoch 74/100
218/218 [==============================] - 0s 928us/step - loss: 0.4647 - val_loss: 0.4825
Epoch 75/100
218/218 [==============================] - 0s 935us/step - loss: 0.4644 - val_loss: 0.4827
Epoch 76/100
218/218 [==============================] - 0s 951us/step - loss: 0.4658 - val_loss: 0.4762
Epoch 77/100
218/218 [==============================] - 0s 961us/step - loss: 0.4655 - val_loss: 0.4783
Epoch 78/100
218/218 [==============================] - 0s 964us/step - loss: 0.4651 - val_loss: 0.4798
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4679 - val_loss: 0.4755
Epoch 80/100
218/218 [==============================] - 0s 950us/step - loss: 0.4652 - val_loss: 0.4820
Epoch 81/100
218/218 [==============================] - 0s 969us/step - loss: 0.4661 - val_loss: 0.4738
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4638 - val_loss: 0.4820
Epoch 83/100
218/218 [==============================] - 0s 956us/step - loss: 0.4643 - val_loss: 0.4786
Epoch 84/100
218/218 [==============================] - 0s 953us/step - loss: 0.4644 - val_loss: 0.4784
Epoch 85/100
218/218 [==============================] - 0s 954us/step - loss: 0.4619 - val_loss: 0.4772
Epoch 86/100
218/218 [==============================] - 0s 960us/step - loss: 0.4644 - val_loss: 0.4792
Epoch 87/100
218/218 [==============================] - 0s 957us/step - loss: 0.4650 - val_loss: 0.4791
Epoch 88/100
218/218 [==============================] - 0s 951us/step - loss: 0.4656 - val_loss: 0.4770
Epoch 89/100
218/218 [==============================] - 0s 949us/step - loss: 0.4650 - val_loss: 0.4762
Epoch 90/100
218/218 [==============================] - 0s 961us/step - loss: 0.4628 - val_loss: 0.4759
Epoch 91/100
218/218 [==============================] - 0s 955us/step - loss: 0.4629 - val_loss: 0.4795
Epoch 92/100
218/218 [==============================] - 0s 960us/step - loss: 0.4626 - val_loss: 0.4768
Epoch 93/100
218/218 [==============================] - 0s 954us/step - loss: 0.4621 - val_loss: 0.4768
Epoch 94/100
218/218 [==============================] - 0s 955us/step - loss: 0.4626 - val_loss: 0.4760
Epoch 95/100
218/218 [==============================] - 0s 961us/step - loss: 0.4632 - val_loss: 0.4747
Epoch 96/100
218/218 [==============================] - 0s 961us/step - loss: 0.4609 - val_loss: 0.4758
Epoch 97/100
218/218 [==============================] - 0s 955us/step - loss: 0.4623 - val_loss: 0.4733
Epoch 98/100
218/218 [==============================] - 0s 954us/step - loss: 0.4604 - val_loss: 0.4742
Epoch 99/100
218/218 [==============================] - 0s 956us/step - loss: 0.4621 - val_loss: 0.4737
Epoch 100/100
218/218 [==============================] - 0s 963us/step - loss: 0.4619 - val_loss: 0.4740
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.10 seconds.
Calculating affinities...
Calculated affinities in 0.31 seconds.
Calculated graph and diffusion operator in 13.43 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.88 seconds.
Calculating KMeans...
Calculated KMeans in 16.07 seconds.
Calculated landmark operator in 18.41 seconds.
Calculating optimal t...
Automatically selected t = 30
Calculated optimal t in 2.86 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.63 seconds.
Calculating metric MDS...
Calculated metric MDS in 17.01 seconds.
Calculated PHATE in 52.36 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_347 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_348 (Dense) (None, 32) 2080 dense_347[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_348[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_348[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_349 (Dense) (None, 32) 128
_________________________________________________________________
dense_350 (Dense) (None, 64) 2112
_________________________________________________________________
dense_351 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_347 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_348 (Dense) (None, 32) 2080 dense_347[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_348[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_348[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1150 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1150 [()] 0 tf_op_layer_Shape_1150[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_115 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1150[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_115[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_345 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_115 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_345[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1151 (TensorF [(2,)] 0 tf_op_layer_Add_115[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1153 (TensorF [(2,)] 0 tf_op_layer_Add_115[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1152 (TensorF [(2,)] 0 tf_op_layer_Add_115[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1154 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1156 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1155 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1157 (TensorF [(2,)] 0 tf_op_layer_Add_115[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1159 (TensorF [(2,)] 0 tf_op_layer_Add_115[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1158 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1151 [()] 0 tf_op_layer_Shape_1151[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1153 [()] 0 tf_op_layer_Shape_1153[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1152 [()] 0 tf_op_layer_Shape_1152[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1154 [()] 0 tf_op_layer_Shape_1154[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1156 [()] 0 tf_op_layer_Shape_1156[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1155 [()] 0 tf_op_layer_Shape_1155[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1157 [()] 0 tf_op_layer_Shape_1157[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1159 [()] 0 tf_op_layer_Shape_1159[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1158 [()] 0 tf_op_layer_Shape_1158[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_690/shape ( [(3,)] 0 tf_op_layer_strided_slice_1151[0]
tf_op_layer_strided_slice_1153[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_691/shape ( [(3,)] 0 tf_op_layer_strided_slice_1152[0]
tf_op_layer_strided_slice_1153[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_692/shape ( [(3,)] 0 tf_op_layer_strided_slice_1154[0]
tf_op_layer_strided_slice_1156[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_693/shape ( [(3,)] 0 tf_op_layer_strided_slice_1155[0]
tf_op_layer_strided_slice_1156[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_694/shape ( [(3,)] 0 tf_op_layer_strided_slice_1157[0]
tf_op_layer_strided_slice_1159[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_695/shape ( [(3,)] 0 tf_op_layer_strided_slice_1158[0]
tf_op_layer_strided_slice_1159[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_690 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_115[0][0]
tf_op_layer_Reshape_690/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_690/multiples [(3,)] 0 tf_op_layer_strided_slice_1152[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_691 (Tensor [(1, None, None)] 0 tf_op_layer_Add_115[0][0]
tf_op_layer_Reshape_691/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_691/multiples [(3,)] 0 tf_op_layer_strided_slice_1151[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_692 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_692/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_692/multiples [(3,)] 0 tf_op_layer_strided_slice_1155[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_693 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_693/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_693/multiples [(3,)] 0 tf_op_layer_strided_slice_1154[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_694 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_115[0][0]
tf_op_layer_Reshape_694/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_694/multiples [(3,)] 0 tf_op_layer_strided_slice_1158[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_695 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_695/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_695/multiples [(3,)] 0 tf_op_layer_strided_slice_1157[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_690 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_690[0][0]
tf_op_layer_Tile_690/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_691 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_691[0][0]
tf_op_layer_Tile_691/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_692 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_692[0][0]
tf_op_layer_Tile_692/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_693 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_693[0][0]
tf_op_layer_Tile_693/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_694 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_694[0][0]
tf_op_layer_Tile_694/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_695 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_695[0][0]
tf_op_layer_Tile_695/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_575 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_690[0][0]
tf_op_layer_Tile_691[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_576 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_692[0][0]
tf_op_layer_Tile_693[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_577 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_694[0][0]
tf_op_layer_Tile_695[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_460 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_575[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_461 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_576[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_462 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_577[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_805 (TensorFlo [(None, None)] 0 tf_op_layer_Square_460[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_806 (TensorFlo [(None, None)] 0 tf_op_layer_Square_461[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_807 (TensorFlo [(None, None)] 0 tf_op_layer_Square_462[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_345 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_805[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_345 (TensorFlo [()] 0 tf_op_layer_strided_slice_1153[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_346 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_806[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_346 (TensorFlo [()] 0 tf_op_layer_strided_slice_1156[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_347 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_807[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_347 (TensorFlo [()] 0 tf_op_layer_strided_slice_1159[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_345 (Tensor [(None, None)] 0 tf_op_layer_Neg_345[0][0]
tf_op_layer_Cast_345[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_346 (Tensor [(None, None)] 0 tf_op_layer_Neg_346[0][0]
tf_op_layer_Cast_346[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_347 (Tensor [(None, None)] 0 tf_op_layer_Neg_347[0][0]
tf_op_layer_Cast_347[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_345 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_345[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_346 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_346[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_347 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_347[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_808 (TensorFlo [()] 0 tf_op_layer_Exp_345[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_809 (TensorFlo [()] 0 tf_op_layer_Exp_346[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_810 (TensorFlo [()] 0 tf_op_layer_Exp_347[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_579 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_230 (TensorFl [()] 0 tf_op_layer_Mean_808[0][0]
tf_op_layer_Mean_809[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_346 (TensorFlow [()] 0 tf_op_layer_Mean_810[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_463 (TensorF [(None, 97)] 0 tf_op_layer_Sub_579[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_578 (TensorFlow [()] 0 tf_op_layer_AddV2_230[0][0]
tf_op_layer_Mul_346[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_115 (TensorFlow [(None,)] 0 tf_op_layer_Square_463[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_347 (TensorFlow [()] 0 tf_op_layer_Sub_578[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_231 (TensorFl [(None,)] 0 tf_op_layer_Sum_115[0][0]
tf_op_layer_Mul_347[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_811 (TensorFlo [()] 0 tf_op_layer_AddV2_231[0][0]
__________________________________________________________________________________________________
add_loss_115 (AddLoss) () 0 tf_op_layer_Mean_811[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 8.1959WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.1045s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 1.0691 - val_loss: 0.5513
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4904 - val_loss: 0.4530
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4140 - val_loss: 0.4081
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3913 - val_loss: 0.3981
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3836 - val_loss: 0.3939
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3768 - val_loss: 0.3901
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3745 - val_loss: 0.3823
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3728 - val_loss: 0.3822
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3673 - val_loss: 0.3819
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3662 - val_loss: 0.3763
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3659 - val_loss: 0.3750
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3634 - val_loss: 0.3751
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3606 - val_loss: 0.3717
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3605 - val_loss: 0.3753
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3593 - val_loss: 0.3719
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3576 - val_loss: 0.3725
Epoch 17/100
218/218 [==============================] - 0s 957us/step - loss: 0.3572 - val_loss: 0.3676
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3550 - val_loss: 0.3685
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3549 - val_loss: 0.3674
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3553 - val_loss: 0.3664
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3540 - val_loss: 0.3655
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3531 - val_loss: 0.3661
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3522 - val_loss: 0.3643
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3516 - val_loss: 0.3663
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3523 - val_loss: 0.3674
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3519 - val_loss: 0.3634
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3509 - val_loss: 0.3620
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3639
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3501 - val_loss: 0.3636
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3498 - val_loss: 0.3610
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3626
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3612
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3499 - val_loss: 0.3619
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3495 - val_loss: 0.3622
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3621
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3621
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3649
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3631
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3595
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3612
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3603
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3606
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3602
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3610
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3606
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3552
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3590
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3570
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3598
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3584
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3565
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3578
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3598
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3613
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3589
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3582
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3565
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3561
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3604
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3455 - val_loss: 0.3633
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3598
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3577
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3600
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3577
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3582
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3547
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3590
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3581
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3576
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3579
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3591
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3584
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3565
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3571
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3556
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3548
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3557
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3579
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3565
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3584
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3575
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3597
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3537
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3570
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3561
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3529
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3574
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3561
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3566
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3566
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3562
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3571
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3545
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3569
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3528
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3555
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3579
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3538
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3562
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3570
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_352 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1160 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1160 [()] 0 tf_op_layer_Shape_1160[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_116 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1160[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_116[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_348 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_116 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_348[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1161 (TensorF [(2,)] 0 tf_op_layer_Add_116[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1163 (TensorF [(2,)] 0 tf_op_layer_Add_116[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1162 (TensorF [(2,)] 0 tf_op_layer_Add_116[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1164 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1166 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1165 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1167 (TensorF [(2,)] 0 tf_op_layer_Add_116[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1169 (TensorF [(2,)] 0 tf_op_layer_Add_116[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1168 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1161 [()] 0 tf_op_layer_Shape_1161[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1163 [()] 0 tf_op_layer_Shape_1163[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1162 [()] 0 tf_op_layer_Shape_1162[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1164 [()] 0 tf_op_layer_Shape_1164[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1166 [()] 0 tf_op_layer_Shape_1166[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1165 [()] 0 tf_op_layer_Shape_1165[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1167 [()] 0 tf_op_layer_Shape_1167[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1169 [()] 0 tf_op_layer_Shape_1169[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1168 [()] 0 tf_op_layer_Shape_1168[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_696/shape ( [(3,)] 0 tf_op_layer_strided_slice_1161[0]
tf_op_layer_strided_slice_1163[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_697/shape ( [(3,)] 0 tf_op_layer_strided_slice_1162[0]
tf_op_layer_strided_slice_1163[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_698/shape ( [(3,)] 0 tf_op_layer_strided_slice_1164[0]
tf_op_layer_strided_slice_1166[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_699/shape ( [(3,)] 0 tf_op_layer_strided_slice_1165[0]
tf_op_layer_strided_slice_1166[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_700/shape ( [(3,)] 0 tf_op_layer_strided_slice_1167[0]
tf_op_layer_strided_slice_1169[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_701/shape ( [(3,)] 0 tf_op_layer_strided_slice_1168[0]
tf_op_layer_strided_slice_1169[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_696 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_116[0][0]
tf_op_layer_Reshape_696/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_696/multiples [(3,)] 0 tf_op_layer_strided_slice_1162[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_697 (Tensor [(1, None, None)] 0 tf_op_layer_Add_116[0][0]
tf_op_layer_Reshape_697/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_697/multiples [(3,)] 0 tf_op_layer_strided_slice_1161[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_698 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_698/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_698/multiples [(3,)] 0 tf_op_layer_strided_slice_1165[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_699 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_699/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_699/multiples [(3,)] 0 tf_op_layer_strided_slice_1164[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_700 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_116[0][0]
tf_op_layer_Reshape_700/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_700/multiples [(3,)] 0 tf_op_layer_strided_slice_1168[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_701 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_701/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_701/multiples [(3,)] 0 tf_op_layer_strided_slice_1167[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_696 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_696[0][0]
tf_op_layer_Tile_696/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_697 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_697[0][0]
tf_op_layer_Tile_697/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_698 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_698[0][0]
tf_op_layer_Tile_698/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_699 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_699[0][0]
tf_op_layer_Tile_699/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_700 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_700[0][0]
tf_op_layer_Tile_700/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_701 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_701[0][0]
tf_op_layer_Tile_701/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_580 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_696[0][0]
tf_op_layer_Tile_697[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_581 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_698[0][0]
tf_op_layer_Tile_699[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_582 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_700[0][0]
tf_op_layer_Tile_701[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_464 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_580[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_465 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_581[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_466 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_582[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_812 (TensorFlo [(None, None)] 0 tf_op_layer_Square_464[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_813 (TensorFlo [(None, None)] 0 tf_op_layer_Square_465[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_814 (TensorFlo [(None, None)] 0 tf_op_layer_Square_466[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_348 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_812[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_348 (TensorFlo [()] 0 tf_op_layer_strided_slice_1163[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_349 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_813[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_349 (TensorFlo [()] 0 tf_op_layer_strided_slice_1166[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_350 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_814[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_350 (TensorFlo [()] 0 tf_op_layer_strided_slice_1169[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_348 (Tensor [(None, None)] 0 tf_op_layer_Neg_348[0][0]
tf_op_layer_Cast_348[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_349 (Tensor [(None, None)] 0 tf_op_layer_Neg_349[0][0]
tf_op_layer_Cast_349[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_350 (Tensor [(None, None)] 0 tf_op_layer_Neg_350[0][0]
tf_op_layer_Cast_350[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_348 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_348[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_349 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_349[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_350 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_350[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_815 (TensorFlo [()] 0 tf_op_layer_Exp_348[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_816 (TensorFlo [()] 0 tf_op_layer_Exp_349[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_817 (TensorFlo [()] 0 tf_op_layer_Exp_350[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_584 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_232 (TensorFl [()] 0 tf_op_layer_Mean_815[0][0]
tf_op_layer_Mean_816[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_349 (TensorFlow [()] 0 tf_op_layer_Mean_817[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_467 (TensorF [(None, 97)] 0 tf_op_layer_Sub_584[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_583 (TensorFlow [()] 0 tf_op_layer_AddV2_232[0][0]
tf_op_layer_Mul_349[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_116 (TensorFlow [(None,)] 0 tf_op_layer_Square_467[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_350 (TensorFlow [()] 0 tf_op_layer_Sub_583[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_233 (TensorFl [(None,)] 0 tf_op_layer_Sum_116[0][0]
tf_op_layer_Mul_350[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_818 (TensorFlo [()] 0 tf_op_layer_AddV2_233[0][0]
__________________________________________________________________________________________________
add_loss_116 (AddLoss) () 0 tf_op_layer_Mean_818[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 7.7649WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.1151s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 2.0721 - val_loss: 0.9886
Epoch 2/100
218/218 [==============================] - 0s 930us/step - loss: 0.8485 - val_loss: 0.7824
Epoch 3/100
218/218 [==============================] - 0s 935us/step - loss: 0.7132 - val_loss: 0.6839
Epoch 4/100
218/218 [==============================] - 0s 923us/step - loss: 0.6450 - val_loss: 0.6461
Epoch 5/100
218/218 [==============================] - 0s 928us/step - loss: 0.6088 - val_loss: 0.6046
Epoch 6/100
218/218 [==============================] - 0s 932us/step - loss: 0.5867 - val_loss: 0.5882
Epoch 7/100
218/218 [==============================] - 0s 929us/step - loss: 0.5683 - val_loss: 0.5866
Epoch 8/100
218/218 [==============================] - 0s 932us/step - loss: 0.5581 - val_loss: 0.5736
Epoch 9/100
218/218 [==============================] - 0s 941us/step - loss: 0.5445 - val_loss: 0.5630
Epoch 10/100
218/218 [==============================] - 0s 938us/step - loss: 0.5380 - val_loss: 0.5537
Epoch 11/100
218/218 [==============================] - 0s 931us/step - loss: 0.5343 - val_loss: 0.5516
Epoch 12/100
218/218 [==============================] - 0s 932us/step - loss: 0.5243 - val_loss: 0.5422
Epoch 13/100
218/218 [==============================] - 0s 980us/step - loss: 0.5204 - val_loss: 0.5453
Epoch 14/100
218/218 [==============================] - 0s 935us/step - loss: 0.5170 - val_loss: 0.5336
Epoch 15/100
218/218 [==============================] - 0s 942us/step - loss: 0.5136 - val_loss: 0.5371
Epoch 16/100
218/218 [==============================] - 0s 931us/step - loss: 0.5120 - val_loss: 0.5260
Epoch 17/100
218/218 [==============================] - 0s 930us/step - loss: 0.5087 - val_loss: 0.5187
Epoch 18/100
218/218 [==============================] - 0s 942us/step - loss: 0.5056 - val_loss: 0.5196
Epoch 19/100
218/218 [==============================] - 0s 910us/step - loss: 0.5018 - val_loss: 0.5177
Epoch 20/100
218/218 [==============================] - 0s 944us/step - loss: 0.4971 - val_loss: 0.5193
Epoch 21/100
218/218 [==============================] - 0s 939us/step - loss: 0.4957 - val_loss: 0.5166
Epoch 22/100
218/218 [==============================] - 0s 937us/step - loss: 0.4962 - val_loss: 0.5113
Epoch 23/100
218/218 [==============================] - 0s 933us/step - loss: 0.4921 - val_loss: 0.5080
Epoch 24/100
218/218 [==============================] - 0s 937us/step - loss: 0.4918 - val_loss: 0.5073
Epoch 25/100
218/218 [==============================] - 0s 944us/step - loss: 0.4917 - val_loss: 0.5075
Epoch 26/100
218/218 [==============================] - 0s 931us/step - loss: 0.4871 - val_loss: 0.5052
Epoch 27/100
218/218 [==============================] - 0s 928us/step - loss: 0.4867 - val_loss: 0.5089
Epoch 28/100
218/218 [==============================] - 0s 923us/step - loss: 0.4846 - val_loss: 0.5023
Epoch 29/100
218/218 [==============================] - 0s 928us/step - loss: 0.4837 - val_loss: 0.5066
Epoch 30/100
218/218 [==============================] - 0s 932us/step - loss: 0.4832 - val_loss: 0.4973
Epoch 31/100
218/218 [==============================] - 0s 934us/step - loss: 0.4800 - val_loss: 0.5066
Epoch 32/100
218/218 [==============================] - 0s 926us/step - loss: 0.4824 - val_loss: 0.5005
Epoch 33/100
218/218 [==============================] - 0s 926us/step - loss: 0.4802 - val_loss: 0.4991
Epoch 34/100
218/218 [==============================] - 0s 925us/step - loss: 0.4774 - val_loss: 0.5004
Epoch 35/100
218/218 [==============================] - 0s 936us/step - loss: 0.4752 - val_loss: 0.4970
Epoch 36/100
218/218 [==============================] - 0s 931us/step - loss: 0.4780 - val_loss: 0.4956
Epoch 37/100
218/218 [==============================] - 0s 933us/step - loss: 0.4746 - val_loss: 0.4993
Epoch 38/100
218/218 [==============================] - 0s 929us/step - loss: 0.4771 - val_loss: 0.5043
Epoch 39/100
218/218 [==============================] - 0s 928us/step - loss: 0.4755 - val_loss: 0.4957
Epoch 40/100
218/218 [==============================] - 0s 929us/step - loss: 0.4729 - val_loss: 0.4964
Epoch 41/100
218/218 [==============================] - 0s 933us/step - loss: 0.4740 - val_loss: 0.4948
Epoch 42/100
218/218 [==============================] - 0s 928us/step - loss: 0.4730 - val_loss: 0.4857
Epoch 43/100
218/218 [==============================] - 0s 929us/step - loss: 0.4737 - val_loss: 0.4894
Epoch 44/100
218/218 [==============================] - 0s 933us/step - loss: 0.4725 - val_loss: 0.4933
Epoch 45/100
218/218 [==============================] - 0s 933us/step - loss: 0.4713 - val_loss: 0.4903
Epoch 46/100
218/218 [==============================] - 0s 932us/step - loss: 0.4700 - val_loss: 0.4917
Epoch 47/100
218/218 [==============================] - 0s 930us/step - loss: 0.4696 - val_loss: 0.4857
Epoch 48/100
218/218 [==============================] - 0s 927us/step - loss: 0.4717 - val_loss: 0.4912
Epoch 49/100
218/218 [==============================] - 0s 929us/step - loss: 0.4687 - val_loss: 0.4858
Epoch 50/100
218/218 [==============================] - 0s 936us/step - loss: 0.4675 - val_loss: 0.4858
Epoch 51/100
218/218 [==============================] - 0s 928us/step - loss: 0.4691 - val_loss: 0.4885
Epoch 52/100
218/218 [==============================] - 0s 933us/step - loss: 0.4671 - val_loss: 0.4909
Epoch 53/100
218/218 [==============================] - 0s 940us/step - loss: 0.4682 - val_loss: 0.4925
Epoch 54/100
218/218 [==============================] - 0s 934us/step - loss: 0.4693 - val_loss: 0.4904
Epoch 55/100
218/218 [==============================] - 0s 932us/step - loss: 0.4663 - val_loss: 0.4883
Epoch 56/100
218/218 [==============================] - 0s 933us/step - loss: 0.4656 - val_loss: 0.4843
Epoch 57/100
218/218 [==============================] - 0s 967us/step - loss: 0.4655 - val_loss: 0.4862
Epoch 58/100
218/218 [==============================] - 0s 943us/step - loss: 0.4670 - val_loss: 0.4898
Epoch 59/100
218/218 [==============================] - 0s 935us/step - loss: 0.4655 - val_loss: 0.4831
Epoch 60/100
218/218 [==============================] - 0s 941us/step - loss: 0.4646 - val_loss: 0.4869
Epoch 61/100
218/218 [==============================] - 0s 932us/step - loss: 0.4656 - val_loss: 0.4867
Epoch 62/100
218/218 [==============================] - 0s 935us/step - loss: 0.4648 - val_loss: 0.4877
Epoch 63/100
218/218 [==============================] - 0s 932us/step - loss: 0.4657 - val_loss: 0.4847
Epoch 64/100
218/218 [==============================] - 0s 933us/step - loss: 0.4658 - val_loss: 0.4897
Epoch 65/100
218/218 [==============================] - 0s 942us/step - loss: 0.4640 - val_loss: 0.4822
Epoch 66/100
218/218 [==============================] - 0s 935us/step - loss: 0.4641 - val_loss: 0.4849
Epoch 67/100
218/218 [==============================] - 0s 927us/step - loss: 0.4633 - val_loss: 0.4836
Epoch 68/100
218/218 [==============================] - 0s 948us/step - loss: 0.4650 - val_loss: 0.4839
Epoch 69/100
218/218 [==============================] - 0s 946us/step - loss: 0.4627 - val_loss: 0.4854
Epoch 70/100
218/218 [==============================] - 0s 932us/step - loss: 0.4634 - val_loss: 0.4836
Epoch 71/100
218/218 [==============================] - 0s 934us/step - loss: 0.4629 - val_loss: 0.4814
Epoch 72/100
218/218 [==============================] - 0s 945us/step - loss: 0.4629 - val_loss: 0.4850
Epoch 73/100
218/218 [==============================] - 0s 930us/step - loss: 0.4613 - val_loss: 0.4812
Epoch 74/100
218/218 [==============================] - 0s 940us/step - loss: 0.4613 - val_loss: 0.4818
Epoch 75/100
218/218 [==============================] - 0s 927us/step - loss: 0.4621 - val_loss: 0.4809
Epoch 76/100
218/218 [==============================] - 0s 927us/step - loss: 0.4614 - val_loss: 0.4800
Epoch 77/100
218/218 [==============================] - 0s 930us/step - loss: 0.4620 - val_loss: 0.4860
Epoch 78/100
218/218 [==============================] - 0s 928us/step - loss: 0.4613 - val_loss: 0.4825
Epoch 79/100
218/218 [==============================] - 0s 931us/step - loss: 0.4615 - val_loss: 0.4777
Epoch 80/100
218/218 [==============================] - 0s 932us/step - loss: 0.4597 - val_loss: 0.4771
Epoch 81/100
218/218 [==============================] - 0s 944us/step - loss: 0.4621 - val_loss: 0.4806
Epoch 82/100
218/218 [==============================] - 0s 949us/step - loss: 0.4607 - val_loss: 0.4795
Epoch 83/100
218/218 [==============================] - 0s 951us/step - loss: 0.4611 - val_loss: 0.4824
Epoch 84/100
218/218 [==============================] - 0s 960us/step - loss: 0.4597 - val_loss: 0.4759
Epoch 85/100
218/218 [==============================] - 0s 960us/step - loss: 0.4604 - val_loss: 0.4810
Epoch 86/100
218/218 [==============================] - 0s 956us/step - loss: 0.4594 - val_loss: 0.4791
Epoch 87/100
218/218 [==============================] - 0s 955us/step - loss: 0.4602 - val_loss: 0.4754
Epoch 88/100
218/218 [==============================] - 0s 955us/step - loss: 0.4597 - val_loss: 0.4769
Epoch 89/100
218/218 [==============================] - 0s 962us/step - loss: 0.4589 - val_loss: 0.4803
Epoch 90/100
218/218 [==============================] - 0s 953us/step - loss: 0.4596 - val_loss: 0.4776
Epoch 91/100
218/218 [==============================] - 0s 959us/step - loss: 0.4586 - val_loss: 0.4771
Epoch 92/100
218/218 [==============================] - 0s 957us/step - loss: 0.4573 - val_loss: 0.4746
Epoch 93/100
218/218 [==============================] - 0s 957us/step - loss: 0.4619 - val_loss: 0.4772
Epoch 94/100
218/218 [==============================] - 0s 967us/step - loss: 0.4581 - val_loss: 0.4760
Epoch 95/100
218/218 [==============================] - 0s 960us/step - loss: 0.4575 - val_loss: 0.4777
Epoch 96/100
218/218 [==============================] - 0s 969us/step - loss: 0.4592 - val_loss: 0.4775
Epoch 97/100
218/218 [==============================] - 0s 968us/step - loss: 0.4574 - val_loss: 0.4781
Epoch 98/100
218/218 [==============================] - 0s 956us/step - loss: 0.4589 - val_loss: 0.4797
Epoch 99/100
218/218 [==============================] - 0s 959us/step - loss: 0.4581 - val_loss: 0.4792
Epoch 100/100
218/218 [==============================] - 0s 961us/step - loss: 0.4591 - val_loss: 0.4760
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.03 seconds.
Calculating affinities...
Calculated affinities in 0.26 seconds.
Calculated graph and diffusion operator in 13.30 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.86 seconds.
Calculating KMeans...
Calculated KMeans in 14.52 seconds.
Calculated landmark operator in 16.85 seconds.
Calculating optimal t...
Automatically selected t = 27
Calculated optimal t in 2.96 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.62 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.10 seconds.
Calculated PHATE in 49.84 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_353 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_354 (Dense) (None, 32) 2080 dense_353[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_354[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_354[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_355 (Dense) (None, 32) 128
_________________________________________________________________
dense_356 (Dense) (None, 64) 2112
_________________________________________________________________
dense_357 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_353 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_354 (Dense) (None, 32) 2080 dense_353[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_354[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_354[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1170 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1170 [()] 0 tf_op_layer_Shape_1170[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_117 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1170[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_117[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_351 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_117 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_351[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1171 (TensorF [(2,)] 0 tf_op_layer_Add_117[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1173 (TensorF [(2,)] 0 tf_op_layer_Add_117[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1172 (TensorF [(2,)] 0 tf_op_layer_Add_117[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1174 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1176 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1175 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1177 (TensorF [(2,)] 0 tf_op_layer_Add_117[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1179 (TensorF [(2,)] 0 tf_op_layer_Add_117[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1178 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1171 [()] 0 tf_op_layer_Shape_1171[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1173 [()] 0 tf_op_layer_Shape_1173[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1172 [()] 0 tf_op_layer_Shape_1172[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1174 [()] 0 tf_op_layer_Shape_1174[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1176 [()] 0 tf_op_layer_Shape_1176[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1175 [()] 0 tf_op_layer_Shape_1175[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1177 [()] 0 tf_op_layer_Shape_1177[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1179 [()] 0 tf_op_layer_Shape_1179[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1178 [()] 0 tf_op_layer_Shape_1178[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_702/shape ( [(3,)] 0 tf_op_layer_strided_slice_1171[0]
tf_op_layer_strided_slice_1173[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_703/shape ( [(3,)] 0 tf_op_layer_strided_slice_1172[0]
tf_op_layer_strided_slice_1173[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_704/shape ( [(3,)] 0 tf_op_layer_strided_slice_1174[0]
tf_op_layer_strided_slice_1176[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_705/shape ( [(3,)] 0 tf_op_layer_strided_slice_1175[0]
tf_op_layer_strided_slice_1176[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_706/shape ( [(3,)] 0 tf_op_layer_strided_slice_1177[0]
tf_op_layer_strided_slice_1179[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_707/shape ( [(3,)] 0 tf_op_layer_strided_slice_1178[0]
tf_op_layer_strided_slice_1179[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_702 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_117[0][0]
tf_op_layer_Reshape_702/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_702/multiples [(3,)] 0 tf_op_layer_strided_slice_1172[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_703 (Tensor [(1, None, None)] 0 tf_op_layer_Add_117[0][0]
tf_op_layer_Reshape_703/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_703/multiples [(3,)] 0 tf_op_layer_strided_slice_1171[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_704 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_704/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_704/multiples [(3,)] 0 tf_op_layer_strided_slice_1175[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_705 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_705/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_705/multiples [(3,)] 0 tf_op_layer_strided_slice_1174[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_706 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_117[0][0]
tf_op_layer_Reshape_706/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_706/multiples [(3,)] 0 tf_op_layer_strided_slice_1178[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_707 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_707/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_707/multiples [(3,)] 0 tf_op_layer_strided_slice_1177[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_702 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_702[0][0]
tf_op_layer_Tile_702/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_703 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_703[0][0]
tf_op_layer_Tile_703/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_704 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_704[0][0]
tf_op_layer_Tile_704/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_705 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_705[0][0]
tf_op_layer_Tile_705/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_706 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_706[0][0]
tf_op_layer_Tile_706/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_707 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_707[0][0]
tf_op_layer_Tile_707/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_585 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_702[0][0]
tf_op_layer_Tile_703[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_586 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_704[0][0]
tf_op_layer_Tile_705[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_587 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_706[0][0]
tf_op_layer_Tile_707[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_468 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_585[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_469 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_586[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_470 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_587[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_819 (TensorFlo [(None, None)] 0 tf_op_layer_Square_468[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_820 (TensorFlo [(None, None)] 0 tf_op_layer_Square_469[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_821 (TensorFlo [(None, None)] 0 tf_op_layer_Square_470[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_351 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_819[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_351 (TensorFlo [()] 0 tf_op_layer_strided_slice_1173[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_352 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_820[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_352 (TensorFlo [()] 0 tf_op_layer_strided_slice_1176[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_353 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_821[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_353 (TensorFlo [()] 0 tf_op_layer_strided_slice_1179[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_351 (Tensor [(None, None)] 0 tf_op_layer_Neg_351[0][0]
tf_op_layer_Cast_351[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_352 (Tensor [(None, None)] 0 tf_op_layer_Neg_352[0][0]
tf_op_layer_Cast_352[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_353 (Tensor [(None, None)] 0 tf_op_layer_Neg_353[0][0]
tf_op_layer_Cast_353[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_351 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_351[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_352 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_352[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_353 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_353[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_822 (TensorFlo [()] 0 tf_op_layer_Exp_351[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_823 (TensorFlo [()] 0 tf_op_layer_Exp_352[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_824 (TensorFlo [()] 0 tf_op_layer_Exp_353[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_589 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_234 (TensorFl [()] 0 tf_op_layer_Mean_822[0][0]
tf_op_layer_Mean_823[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_352 (TensorFlow [()] 0 tf_op_layer_Mean_824[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_471 (TensorF [(None, 97)] 0 tf_op_layer_Sub_589[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_588 (TensorFlow [()] 0 tf_op_layer_AddV2_234[0][0]
tf_op_layer_Mul_352[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_117 (TensorFlow [(None,)] 0 tf_op_layer_Square_471[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_353 (TensorFlow [()] 0 tf_op_layer_Sub_588[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_235 (TensorFl [(None,)] 0 tf_op_layer_Sum_117[0][0]
tf_op_layer_Mul_353[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_825 (TensorFlo [()] 0 tf_op_layer_AddV2_235[0][0]
__________________________________________________________________________________________________
add_loss_117 (AddLoss) () 0 tf_op_layer_Mean_825[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 9s - loss: 7.9914WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0905s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 0.9310 - val_loss: 0.4762
Epoch 2/100
218/218 [==============================] - 0s 997us/step - loss: 0.4379 - val_loss: 0.3991
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3949 - val_loss: 0.3833
Epoch 4/100
218/218 [==============================] - 0s 995us/step - loss: 0.3829 - val_loss: 0.3798
Epoch 5/100
218/218 [==============================] - 0s 998us/step - loss: 0.3767 - val_loss: 0.3738
Epoch 6/100
218/218 [==============================] - 0s 996us/step - loss: 0.3739 - val_loss: 0.3715
Epoch 7/100
218/218 [==============================] - 0s 994us/step - loss: 0.3700 - val_loss: 0.3691
Epoch 8/100
218/218 [==============================] - 0s 997us/step - loss: 0.3675 - val_loss: 0.3653
Epoch 9/100
218/218 [==============================] - 0s 995us/step - loss: 0.3655 - val_loss: 0.3631
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3628 - val_loss: 0.3658
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3630 - val_loss: 0.3652
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3615 - val_loss: 0.3587
Epoch 13/100
218/218 [==============================] - 0s 997us/step - loss: 0.3595 - val_loss: 0.3585
Epoch 14/100
218/218 [==============================] - 0s 996us/step - loss: 0.3575 - val_loss: 0.3582
Epoch 15/100
218/218 [==============================] - 0s 993us/step - loss: 0.3585 - val_loss: 0.3570
Epoch 16/100
218/218 [==============================] - 0s 999us/step - loss: 0.3567 - val_loss: 0.3592
Epoch 17/100
218/218 [==============================] - 0s 994us/step - loss: 0.3561 - val_loss: 0.3571
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3553 - val_loss: 0.3574
Epoch 19/100
218/218 [==============================] - 0s 995us/step - loss: 0.3552 - val_loss: 0.3531
Epoch 20/100
218/218 [==============================] - 0s 992us/step - loss: 0.3533 - val_loss: 0.3561
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3524 - val_loss: 0.3550
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3526 - val_loss: 0.3553
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3525 - val_loss: 0.3517
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3529
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3506 - val_loss: 0.3520
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3509 - val_loss: 0.3537
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3505 - val_loss: 0.3518
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3515 - val_loss: 0.3526
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3499 - val_loss: 0.3519
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3506 - val_loss: 0.3519
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3505 - val_loss: 0.3517
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3497 - val_loss: 0.3544
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3507 - val_loss: 0.3509
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3523
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3504
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3520
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3491
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3509
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3492
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3482 - val_loss: 0.3498
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3509
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3511
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3499
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3503
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3509
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3510
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3487
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3480
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3469
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3469
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3505
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3504
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3484
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3480
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3503
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3491
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3496
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3525
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3478
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3484
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3500
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3470
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3482
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3506
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3490
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3480
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3483
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3469
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3486
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3487
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3475
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3464
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3476
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3486
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3477
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3488
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3488
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3485
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3487
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3470
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3453
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3456
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3460
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3473
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3462
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3479
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3489
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3465
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3462
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3472
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3495
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3475
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3455
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3473
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3473
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3464
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3449
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3445
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3444
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3455
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_358 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1180 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1180 [()] 0 tf_op_layer_Shape_1180[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_118 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1180[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_118[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_354 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_118 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_354[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1181 (TensorF [(2,)] 0 tf_op_layer_Add_118[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1183 (TensorF [(2,)] 0 tf_op_layer_Add_118[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1182 (TensorF [(2,)] 0 tf_op_layer_Add_118[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1184 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1186 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1185 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1187 (TensorF [(2,)] 0 tf_op_layer_Add_118[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1189 (TensorF [(2,)] 0 tf_op_layer_Add_118[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1188 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1181 [()] 0 tf_op_layer_Shape_1181[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1183 [()] 0 tf_op_layer_Shape_1183[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1182 [()] 0 tf_op_layer_Shape_1182[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1184 [()] 0 tf_op_layer_Shape_1184[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1186 [()] 0 tf_op_layer_Shape_1186[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1185 [()] 0 tf_op_layer_Shape_1185[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1187 [()] 0 tf_op_layer_Shape_1187[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1189 [()] 0 tf_op_layer_Shape_1189[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1188 [()] 0 tf_op_layer_Shape_1188[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_708/shape ( [(3,)] 0 tf_op_layer_strided_slice_1181[0]
tf_op_layer_strided_slice_1183[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_709/shape ( [(3,)] 0 tf_op_layer_strided_slice_1182[0]
tf_op_layer_strided_slice_1183[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_710/shape ( [(3,)] 0 tf_op_layer_strided_slice_1184[0]
tf_op_layer_strided_slice_1186[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_711/shape ( [(3,)] 0 tf_op_layer_strided_slice_1185[0]
tf_op_layer_strided_slice_1186[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_712/shape ( [(3,)] 0 tf_op_layer_strided_slice_1187[0]
tf_op_layer_strided_slice_1189[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_713/shape ( [(3,)] 0 tf_op_layer_strided_slice_1188[0]
tf_op_layer_strided_slice_1189[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_708 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_118[0][0]
tf_op_layer_Reshape_708/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_708/multiples [(3,)] 0 tf_op_layer_strided_slice_1182[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_709 (Tensor [(1, None, None)] 0 tf_op_layer_Add_118[0][0]
tf_op_layer_Reshape_709/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_709/multiples [(3,)] 0 tf_op_layer_strided_slice_1181[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_710 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_710/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_710/multiples [(3,)] 0 tf_op_layer_strided_slice_1185[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_711 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_711/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_711/multiples [(3,)] 0 tf_op_layer_strided_slice_1184[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_712 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_118[0][0]
tf_op_layer_Reshape_712/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_712/multiples [(3,)] 0 tf_op_layer_strided_slice_1188[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_713 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_713/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_713/multiples [(3,)] 0 tf_op_layer_strided_slice_1187[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_708 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_708[0][0]
tf_op_layer_Tile_708/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_709 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_709[0][0]
tf_op_layer_Tile_709/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_710 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_710[0][0]
tf_op_layer_Tile_710/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_711 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_711[0][0]
tf_op_layer_Tile_711/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_712 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_712[0][0]
tf_op_layer_Tile_712/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_713 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_713[0][0]
tf_op_layer_Tile_713/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_590 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_708[0][0]
tf_op_layer_Tile_709[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_591 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_710[0][0]
tf_op_layer_Tile_711[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_592 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_712[0][0]
tf_op_layer_Tile_713[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_472 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_590[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_473 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_591[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_474 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_592[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_826 (TensorFlo [(None, None)] 0 tf_op_layer_Square_472[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_827 (TensorFlo [(None, None)] 0 tf_op_layer_Square_473[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_828 (TensorFlo [(None, None)] 0 tf_op_layer_Square_474[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_354 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_826[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_354 (TensorFlo [()] 0 tf_op_layer_strided_slice_1183[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_355 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_827[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_355 (TensorFlo [()] 0 tf_op_layer_strided_slice_1186[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_356 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_828[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_356 (TensorFlo [()] 0 tf_op_layer_strided_slice_1189[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_354 (Tensor [(None, None)] 0 tf_op_layer_Neg_354[0][0]
tf_op_layer_Cast_354[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_355 (Tensor [(None, None)] 0 tf_op_layer_Neg_355[0][0]
tf_op_layer_Cast_355[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_356 (Tensor [(None, None)] 0 tf_op_layer_Neg_356[0][0]
tf_op_layer_Cast_356[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_354 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_354[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_355 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_355[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_356 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_356[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_829 (TensorFlo [()] 0 tf_op_layer_Exp_354[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_830 (TensorFlo [()] 0 tf_op_layer_Exp_355[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_831 (TensorFlo [()] 0 tf_op_layer_Exp_356[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_594 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_236 (TensorFl [()] 0 tf_op_layer_Mean_829[0][0]
tf_op_layer_Mean_830[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_355 (TensorFlow [()] 0 tf_op_layer_Mean_831[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_475 (TensorF [(None, 97)] 0 tf_op_layer_Sub_594[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_593 (TensorFlow [()] 0 tf_op_layer_AddV2_236[0][0]
tf_op_layer_Mul_355[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_118 (TensorFlow [(None,)] 0 tf_op_layer_Square_475[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_356 (TensorFlow [()] 0 tf_op_layer_Sub_593[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_237 (TensorFl [(None,)] 0 tf_op_layer_Sum_118[0][0]
tf_op_layer_Mul_356[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_832 (TensorFlo [()] 0 tf_op_layer_AddV2_237[0][0]
__________________________________________________________________________________________________
add_loss_118 (AddLoss) () 0 tf_op_layer_Mean_832[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 9s - loss: 8.2386WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.0889s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9503 - val_loss: 0.9479
Epoch 2/100
218/218 [==============================] - 0s 933us/step - loss: 0.7921 - val_loss: 0.7459
Epoch 3/100
218/218 [==============================] - 0s 933us/step - loss: 0.6683 - val_loss: 0.6544
Epoch 4/100
218/218 [==============================] - 0s 929us/step - loss: 0.6193 - val_loss: 0.6312
Epoch 5/100
218/218 [==============================] - 0s 942us/step - loss: 0.5921 - val_loss: 0.5954
Epoch 6/100
218/218 [==============================] - 0s 935us/step - loss: 0.5745 - val_loss: 0.5906
Epoch 7/100
218/218 [==============================] - 0s 932us/step - loss: 0.5608 - val_loss: 0.5724
Epoch 8/100
218/218 [==============================] - 0s 933us/step - loss: 0.5518 - val_loss: 0.5668
Epoch 9/100
218/218 [==============================] - 0s 933us/step - loss: 0.5422 - val_loss: 0.5562
Epoch 10/100
218/218 [==============================] - 0s 931us/step - loss: 0.5352 - val_loss: 0.5629
Epoch 11/100
218/218 [==============================] - 0s 925us/step - loss: 0.5291 - val_loss: 0.5446
Epoch 12/100
218/218 [==============================] - 0s 924us/step - loss: 0.5226 - val_loss: 0.5403
Epoch 13/100
218/218 [==============================] - 0s 930us/step - loss: 0.5183 - val_loss: 0.5366
Epoch 14/100
218/218 [==============================] - 0s 928us/step - loss: 0.5164 - val_loss: 0.5358
Epoch 15/100
218/218 [==============================] - 0s 928us/step - loss: 0.5093 - val_loss: 0.5247
Epoch 16/100
218/218 [==============================] - 0s 927us/step - loss: 0.5088 - val_loss: 0.5231
Epoch 17/100
218/218 [==============================] - 0s 925us/step - loss: 0.5078 - val_loss: 0.5202
Epoch 18/100
218/218 [==============================] - 0s 938us/step - loss: 0.5013 - val_loss: 0.5204
Epoch 19/100
218/218 [==============================] - 0s 937us/step - loss: 0.5005 - val_loss: 0.5206
Epoch 20/100
218/218 [==============================] - 0s 929us/step - loss: 0.4980 - val_loss: 0.5221
Epoch 21/100
218/218 [==============================] - 0s 925us/step - loss: 0.4977 - val_loss: 0.5150
Epoch 22/100
218/218 [==============================] - 0s 936us/step - loss: 0.4940 - val_loss: 0.5127
Epoch 23/100
218/218 [==============================] - 0s 932us/step - loss: 0.4953 - val_loss: 0.5103
Epoch 24/100
218/218 [==============================] - 0s 942us/step - loss: 0.4917 - val_loss: 0.5083
Epoch 25/100
218/218 [==============================] - 0s 938us/step - loss: 0.4884 - val_loss: 0.5106
Epoch 26/100
218/218 [==============================] - 0s 933us/step - loss: 0.4916 - val_loss: 0.5087
Epoch 27/100
218/218 [==============================] - 0s 936us/step - loss: 0.4886 - val_loss: 0.5015
Epoch 28/100
218/218 [==============================] - 0s 926us/step - loss: 0.4858 - val_loss: 0.4996
Epoch 29/100
218/218 [==============================] - 0s 939us/step - loss: 0.4842 - val_loss: 0.5006
Epoch 30/100
218/218 [==============================] - 0s 936us/step - loss: 0.4842 - val_loss: 0.5054
Epoch 31/100
218/218 [==============================] - 0s 934us/step - loss: 0.4806 - val_loss: 0.5011
Epoch 32/100
218/218 [==============================] - 0s 933us/step - loss: 0.4813 - val_loss: 0.5005
Epoch 33/100
218/218 [==============================] - 0s 933us/step - loss: 0.4801 - val_loss: 0.4985
Epoch 34/100
218/218 [==============================] - 0s 957us/step - loss: 0.4787 - val_loss: 0.4964
Epoch 35/100
218/218 [==============================] - 0s 938us/step - loss: 0.4761 - val_loss: 0.4986
Epoch 36/100
218/218 [==============================] - 0s 933us/step - loss: 0.4770 - val_loss: 0.4984
Epoch 37/100
218/218 [==============================] - 0s 937us/step - loss: 0.4767 - val_loss: 0.4889
Epoch 38/100
218/218 [==============================] - 0s 937us/step - loss: 0.4751 - val_loss: 0.4952
Epoch 39/100
218/218 [==============================] - 0s 940us/step - loss: 0.4778 - val_loss: 0.4940
Epoch 40/100
218/218 [==============================] - 0s 934us/step - loss: 0.4750 - val_loss: 0.4973
Epoch 41/100
218/218 [==============================] - 0s 940us/step - loss: 0.4758 - val_loss: 0.4952
Epoch 42/100
218/218 [==============================] - 0s 950us/step - loss: 0.4732 - val_loss: 0.4974
Epoch 43/100
218/218 [==============================] - 0s 942us/step - loss: 0.4751 - val_loss: 0.4941
Epoch 44/100
218/218 [==============================] - 0s 936us/step - loss: 0.4727 - val_loss: 0.4941
Epoch 45/100
218/218 [==============================] - 0s 932us/step - loss: 0.4742 - val_loss: 0.4901
Epoch 46/100
218/218 [==============================] - 0s 931us/step - loss: 0.4720 - val_loss: 0.4913
Epoch 47/100
218/218 [==============================] - 0s 930us/step - loss: 0.4712 - val_loss: 0.4881
Epoch 48/100
218/218 [==============================] - 0s 934us/step - loss: 0.4717 - val_loss: 0.4913
Epoch 49/100
218/218 [==============================] - 0s 935us/step - loss: 0.4734 - val_loss: 0.4906
Epoch 50/100
218/218 [==============================] - 0s 931us/step - loss: 0.4724 - val_loss: 0.4894
Epoch 51/100
218/218 [==============================] - 0s 931us/step - loss: 0.4707 - val_loss: 0.4826
Epoch 52/100
218/218 [==============================] - 0s 936us/step - loss: 0.4689 - val_loss: 0.4869
Epoch 53/100
218/218 [==============================] - 0s 983us/step - loss: 0.4698 - val_loss: 0.4842
Epoch 54/100
218/218 [==============================] - 0s 959us/step - loss: 0.4702 - val_loss: 0.4832
Epoch 55/100
218/218 [==============================] - 0s 946us/step - loss: 0.4712 - val_loss: 0.4853
Epoch 56/100
218/218 [==============================] - 0s 939us/step - loss: 0.4676 - val_loss: 0.4825
Epoch 57/100
218/218 [==============================] - 0s 938us/step - loss: 0.4659 - val_loss: 0.4832
Epoch 58/100
218/218 [==============================] - 0s 943us/step - loss: 0.4682 - val_loss: 0.4846
Epoch 59/100
218/218 [==============================] - 0s 939us/step - loss: 0.4684 - val_loss: 0.4873
Epoch 60/100
218/218 [==============================] - 0s 936us/step - loss: 0.4656 - val_loss: 0.4888
Epoch 61/100
218/218 [==============================] - 0s 942us/step - loss: 0.4681 - val_loss: 0.4875
Epoch 62/100
218/218 [==============================] - 0s 939us/step - loss: 0.4671 - val_loss: 0.4880
Epoch 63/100
218/218 [==============================] - 0s 950us/step - loss: 0.4657 - val_loss: 0.4840
Epoch 64/100
218/218 [==============================] - 0s 936us/step - loss: 0.4661 - val_loss: 0.4867
Epoch 65/100
218/218 [==============================] - 0s 952us/step - loss: 0.4668 - val_loss: 0.4848
Epoch 66/100
218/218 [==============================] - 0s 957us/step - loss: 0.4683 - val_loss: 0.4827
Epoch 67/100
218/218 [==============================] - 0s 963us/step - loss: 0.4654 - val_loss: 0.4832
Epoch 68/100
218/218 [==============================] - 0s 936us/step - loss: 0.4638 - val_loss: 0.4817
Epoch 69/100
218/218 [==============================] - 0s 935us/step - loss: 0.4644 - val_loss: 0.4818
Epoch 70/100
218/218 [==============================] - 0s 930us/step - loss: 0.4634 - val_loss: 0.4831
Epoch 71/100
218/218 [==============================] - 0s 934us/step - loss: 0.4656 - val_loss: 0.4811
Epoch 72/100
218/218 [==============================] - 0s 936us/step - loss: 0.4645 - val_loss: 0.4836
Epoch 73/100
218/218 [==============================] - 0s 942us/step - loss: 0.4631 - val_loss: 0.4810
Epoch 74/100
218/218 [==============================] - 0s 941us/step - loss: 0.4635 - val_loss: 0.4868
Epoch 75/100
218/218 [==============================] - 0s 946us/step - loss: 0.4626 - val_loss: 0.4795
Epoch 76/100
218/218 [==============================] - 0s 939us/step - loss: 0.4626 - val_loss: 0.4824
Epoch 77/100
218/218 [==============================] - 0s 936us/step - loss: 0.4650 - val_loss: 0.4792
Epoch 78/100
218/218 [==============================] - 0s 934us/step - loss: 0.4641 - val_loss: 0.4798
Epoch 79/100
218/218 [==============================] - 0s 935us/step - loss: 0.4623 - val_loss: 0.4840
Epoch 80/100
218/218 [==============================] - 0s 928us/step - loss: 0.4619 - val_loss: 0.4827
Epoch 81/100
218/218 [==============================] - 0s 932us/step - loss: 0.4625 - val_loss: 0.4773
Epoch 82/100
218/218 [==============================] - 0s 943us/step - loss: 0.4629 - val_loss: 0.4835
Epoch 83/100
218/218 [==============================] - 0s 935us/step - loss: 0.4610 - val_loss: 0.4816
Epoch 84/100
218/218 [==============================] - 0s 946us/step - loss: 0.4611 - val_loss: 0.4758
Epoch 85/100
218/218 [==============================] - 0s 942us/step - loss: 0.4596 - val_loss: 0.4783
Epoch 86/100
218/218 [==============================] - 0s 939us/step - loss: 0.4626 - val_loss: 0.4867
Epoch 87/100
218/218 [==============================] - 0s 935us/step - loss: 0.4609 - val_loss: 0.4831
Epoch 88/100
218/218 [==============================] - 0s 932us/step - loss: 0.4612 - val_loss: 0.4787
Epoch 89/100
218/218 [==============================] - 0s 931us/step - loss: 0.4614 - val_loss: 0.4808
Epoch 90/100
218/218 [==============================] - 0s 940us/step - loss: 0.4603 - val_loss: 0.4815
Epoch 91/100
218/218 [==============================] - 0s 966us/step - loss: 0.4614 - val_loss: 0.4775
Epoch 92/100
218/218 [==============================] - 0s 964us/step - loss: 0.4602 - val_loss: 0.4769
Epoch 93/100
218/218 [==============================] - 0s 963us/step - loss: 0.4612 - val_loss: 0.4807
Epoch 94/100
218/218 [==============================] - 0s 962us/step - loss: 0.4610 - val_loss: 0.4791
Epoch 95/100
218/218 [==============================] - 0s 955us/step - loss: 0.4607 - val_loss: 0.4773
Epoch 96/100
218/218 [==============================] - 0s 963us/step - loss: 0.4593 - val_loss: 0.4789
Epoch 97/100
218/218 [==============================] - 0s 970us/step - loss: 0.4614 - val_loss: 0.4772
Epoch 98/100
218/218 [==============================] - 0s 959us/step - loss: 0.4601 - val_loss: 0.4781
Epoch 99/100
218/218 [==============================] - 0s 962us/step - loss: 0.4633 - val_loss: 0.4801
Epoch 100/100
218/218 [==============================] - 0s 962us/step - loss: 0.4595 - val_loss: 0.4752
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.25 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.52 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.87 seconds.
Calculating KMeans...
Calculated KMeans in 15.84 seconds.
Calculated landmark operator in 18.16 seconds.
Calculating optimal t...
Automatically selected t = 32
Calculated optimal t in 2.85 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.45 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.92 seconds.
Calculated PHATE in 51.92 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_359 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_360 (Dense) (None, 32) 2080 dense_359[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_360[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_360[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_361 (Dense) (None, 32) 128
_________________________________________________________________
dense_362 (Dense) (None, 64) 2112
_________________________________________________________________
dense_363 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_359 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_360 (Dense) (None, 32) 2080 dense_359[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_360[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_360[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1190 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1190 [()] 0 tf_op_layer_Shape_1190[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_119 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1190[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_119[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_357 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_119 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_357[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1191 (TensorF [(2,)] 0 tf_op_layer_Add_119[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1193 (TensorF [(2,)] 0 tf_op_layer_Add_119[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1192 (TensorF [(2,)] 0 tf_op_layer_Add_119[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1194 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1196 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1195 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1197 (TensorF [(2,)] 0 tf_op_layer_Add_119[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1199 (TensorF [(2,)] 0 tf_op_layer_Add_119[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1198 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1191 [()] 0 tf_op_layer_Shape_1191[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1193 [()] 0 tf_op_layer_Shape_1193[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1192 [()] 0 tf_op_layer_Shape_1192[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1194 [()] 0 tf_op_layer_Shape_1194[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1196 [()] 0 tf_op_layer_Shape_1196[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1195 [()] 0 tf_op_layer_Shape_1195[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1197 [()] 0 tf_op_layer_Shape_1197[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1199 [()] 0 tf_op_layer_Shape_1199[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1198 [()] 0 tf_op_layer_Shape_1198[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_714/shape ( [(3,)] 0 tf_op_layer_strided_slice_1191[0]
tf_op_layer_strided_slice_1193[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_715/shape ( [(3,)] 0 tf_op_layer_strided_slice_1192[0]
tf_op_layer_strided_slice_1193[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_716/shape ( [(3,)] 0 tf_op_layer_strided_slice_1194[0]
tf_op_layer_strided_slice_1196[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_717/shape ( [(3,)] 0 tf_op_layer_strided_slice_1195[0]
tf_op_layer_strided_slice_1196[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_718/shape ( [(3,)] 0 tf_op_layer_strided_slice_1197[0]
tf_op_layer_strided_slice_1199[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_719/shape ( [(3,)] 0 tf_op_layer_strided_slice_1198[0]
tf_op_layer_strided_slice_1199[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_714 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_119[0][0]
tf_op_layer_Reshape_714/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_714/multiples [(3,)] 0 tf_op_layer_strided_slice_1192[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_715 (Tensor [(1, None, None)] 0 tf_op_layer_Add_119[0][0]
tf_op_layer_Reshape_715/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_715/multiples [(3,)] 0 tf_op_layer_strided_slice_1191[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_716 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_716/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_716/multiples [(3,)] 0 tf_op_layer_strided_slice_1195[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_717 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_717/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_717/multiples [(3,)] 0 tf_op_layer_strided_slice_1194[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_718 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_119[0][0]
tf_op_layer_Reshape_718/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_718/multiples [(3,)] 0 tf_op_layer_strided_slice_1198[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_719 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_719/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_719/multiples [(3,)] 0 tf_op_layer_strided_slice_1197[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_714 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_714[0][0]
tf_op_layer_Tile_714/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_715 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_715[0][0]
tf_op_layer_Tile_715/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_716 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_716[0][0]
tf_op_layer_Tile_716/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_717 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_717[0][0]
tf_op_layer_Tile_717/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_718 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_718[0][0]
tf_op_layer_Tile_718/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_719 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_719[0][0]
tf_op_layer_Tile_719/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_595 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_714[0][0]
tf_op_layer_Tile_715[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_596 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_716[0][0]
tf_op_layer_Tile_717[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_597 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_718[0][0]
tf_op_layer_Tile_719[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_476 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_595[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_477 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_596[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_478 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_597[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_833 (TensorFlo [(None, None)] 0 tf_op_layer_Square_476[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_834 (TensorFlo [(None, None)] 0 tf_op_layer_Square_477[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_835 (TensorFlo [(None, None)] 0 tf_op_layer_Square_478[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_357 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_833[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_357 (TensorFlo [()] 0 tf_op_layer_strided_slice_1193[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_358 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_834[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_358 (TensorFlo [()] 0 tf_op_layer_strided_slice_1196[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_359 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_835[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_359 (TensorFlo [()] 0 tf_op_layer_strided_slice_1199[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_357 (Tensor [(None, None)] 0 tf_op_layer_Neg_357[0][0]
tf_op_layer_Cast_357[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_358 (Tensor [(None, None)] 0 tf_op_layer_Neg_358[0][0]
tf_op_layer_Cast_358[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_359 (Tensor [(None, None)] 0 tf_op_layer_Neg_359[0][0]
tf_op_layer_Cast_359[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_357 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_357[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_358 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_358[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_359 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_359[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_836 (TensorFlo [()] 0 tf_op_layer_Exp_357[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_837 (TensorFlo [()] 0 tf_op_layer_Exp_358[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_838 (TensorFlo [()] 0 tf_op_layer_Exp_359[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_599 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_238 (TensorFl [()] 0 tf_op_layer_Mean_836[0][0]
tf_op_layer_Mean_837[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_358 (TensorFlow [()] 0 tf_op_layer_Mean_838[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_479 (TensorF [(None, 97)] 0 tf_op_layer_Sub_599[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_598 (TensorFlow [()] 0 tf_op_layer_AddV2_238[0][0]
tf_op_layer_Mul_358[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_119 (TensorFlow [(None,)] 0 tf_op_layer_Square_479[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_359 (TensorFlow [()] 0 tf_op_layer_Sub_598[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_239 (TensorFl [(None,)] 0 tf_op_layer_Sum_119[0][0]
tf_op_layer_Mul_359[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_839 (TensorFlo [()] 0 tf_op_layer_AddV2_239[0][0]
__________________________________________________________________________________________________
add_loss_119 (AddLoss) () 0 tf_op_layer_Mean_839[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 8.1549WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.1126s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9450 - val_loss: 0.5015
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4249 - val_loss: 0.4107
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3865 - val_loss: 0.3965
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3784 - val_loss: 0.3992
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3732 - val_loss: 0.3887
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3683 - val_loss: 0.3882
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3652 - val_loss: 0.3853
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3633 - val_loss: 0.3786
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3610 - val_loss: 0.3788
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3585 - val_loss: 0.3773
Epoch 11/100
218/218 [==============================] - 0s 951us/step - loss: 0.3564 - val_loss: 0.3744
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3545 - val_loss: 0.3736
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3538 - val_loss: 0.3731
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3710
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3535 - val_loss: 0.3743
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3512 - val_loss: 0.3681
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3501 - val_loss: 0.3690
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3510 - val_loss: 0.3712
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3486 - val_loss: 0.3695
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3703
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3482 - val_loss: 0.3647
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3655
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3486 - val_loss: 0.3670
Epoch 24/100
218/218 [==============================] - 0s 977us/step - loss: 0.3474 - val_loss: 0.3660
Epoch 25/100
218/218 [==============================] - 0s 981us/step - loss: 0.3459 - val_loss: 0.3624
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3655
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3636
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3628
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3619
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3647
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3654
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3627
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3599
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3652
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3618
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3613
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3615
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3615
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3636
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3612
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3584
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3604
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3587
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3620
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3583
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3592
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3577
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3601
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3576
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3389 - val_loss: 0.3605
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3625
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3592
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3564
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3380 - val_loss: 0.3560
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3560
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3395 - val_loss: 0.3581
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3537
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3377 - val_loss: 0.3547
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3377 - val_loss: 0.3558
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3386 - val_loss: 0.3576
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3364 - val_loss: 0.3553
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3368 - val_loss: 0.3566
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3374 - val_loss: 0.3558
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3375 - val_loss: 0.3593
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3371 - val_loss: 0.3534
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3359 - val_loss: 0.3547
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3368 - val_loss: 0.3556
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3360 - val_loss: 0.3552
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3360 - val_loss: 0.3551
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3355 - val_loss: 0.3562
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3355 - val_loss: 0.3534
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3360 - val_loss: 0.3514
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3357 - val_loss: 0.3535
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3348 - val_loss: 0.3548
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3346 - val_loss: 0.3516
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3354 - val_loss: 0.3548
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3353 - val_loss: 0.3506
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3347 - val_loss: 0.3533
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3355 - val_loss: 0.3531
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3353 - val_loss: 0.3541
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3341 - val_loss: 0.3543
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3342 - val_loss: 0.3526
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3340 - val_loss: 0.3523
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3338 - val_loss: 0.3562
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3336 - val_loss: 0.3516
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3340 - val_loss: 0.3531
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3351 - val_loss: 0.3513
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3340 - val_loss: 0.3514
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3340 - val_loss: 0.3519
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3322 - val_loss: 0.3535
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3338 - val_loss: 0.3528
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3336 - val_loss: 0.3515
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3346 - val_loss: 0.3536
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3330 - val_loss: 0.3499
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3336 - val_loss: 0.3529
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3335 - val_loss: 0.3522
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3332 - val_loss: 0.3535
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3334 - val_loss: 0.3510
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3331 - val_loss: 0.3526
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3339 - val_loss: 0.3502
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_364 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1200 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1200 [()] 0 tf_op_layer_Shape_1200[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_120 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1200[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_120[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_360 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_120 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_360[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1201 (TensorF [(2,)] 0 tf_op_layer_Add_120[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1203 (TensorF [(2,)] 0 tf_op_layer_Add_120[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1202 (TensorF [(2,)] 0 tf_op_layer_Add_120[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1204 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1206 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1205 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1207 (TensorF [(2,)] 0 tf_op_layer_Add_120[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1209 (TensorF [(2,)] 0 tf_op_layer_Add_120[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1208 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1201 [()] 0 tf_op_layer_Shape_1201[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1203 [()] 0 tf_op_layer_Shape_1203[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1202 [()] 0 tf_op_layer_Shape_1202[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1204 [()] 0 tf_op_layer_Shape_1204[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1206 [()] 0 tf_op_layer_Shape_1206[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1205 [()] 0 tf_op_layer_Shape_1205[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1207 [()] 0 tf_op_layer_Shape_1207[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1209 [()] 0 tf_op_layer_Shape_1209[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1208 [()] 0 tf_op_layer_Shape_1208[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_720/shape ( [(3,)] 0 tf_op_layer_strided_slice_1201[0]
tf_op_layer_strided_slice_1203[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_721/shape ( [(3,)] 0 tf_op_layer_strided_slice_1202[0]
tf_op_layer_strided_slice_1203[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_722/shape ( [(3,)] 0 tf_op_layer_strided_slice_1204[0]
tf_op_layer_strided_slice_1206[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_723/shape ( [(3,)] 0 tf_op_layer_strided_slice_1205[0]
tf_op_layer_strided_slice_1206[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_724/shape ( [(3,)] 0 tf_op_layer_strided_slice_1207[0]
tf_op_layer_strided_slice_1209[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_725/shape ( [(3,)] 0 tf_op_layer_strided_slice_1208[0]
tf_op_layer_strided_slice_1209[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_720 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_120[0][0]
tf_op_layer_Reshape_720/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_720/multiples [(3,)] 0 tf_op_layer_strided_slice_1202[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_721 (Tensor [(1, None, None)] 0 tf_op_layer_Add_120[0][0]
tf_op_layer_Reshape_721/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_721/multiples [(3,)] 0 tf_op_layer_strided_slice_1201[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_722 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_722/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_722/multiples [(3,)] 0 tf_op_layer_strided_slice_1205[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_723 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_723/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_723/multiples [(3,)] 0 tf_op_layer_strided_slice_1204[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_724 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_120[0][0]
tf_op_layer_Reshape_724/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_724/multiples [(3,)] 0 tf_op_layer_strided_slice_1208[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_725 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_725/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_725/multiples [(3,)] 0 tf_op_layer_strided_slice_1207[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_720 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_720[0][0]
tf_op_layer_Tile_720/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_721 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_721[0][0]
tf_op_layer_Tile_721/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_722 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_722[0][0]
tf_op_layer_Tile_722/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_723 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_723[0][0]
tf_op_layer_Tile_723/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_724 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_724[0][0]
tf_op_layer_Tile_724/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_725 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_725[0][0]
tf_op_layer_Tile_725/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_600 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_720[0][0]
tf_op_layer_Tile_721[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_601 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_722[0][0]
tf_op_layer_Tile_723[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_602 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_724[0][0]
tf_op_layer_Tile_725[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_480 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_600[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_481 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_601[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_482 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_602[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_840 (TensorFlo [(None, None)] 0 tf_op_layer_Square_480[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_841 (TensorFlo [(None, None)] 0 tf_op_layer_Square_481[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_842 (TensorFlo [(None, None)] 0 tf_op_layer_Square_482[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_360 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_840[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_360 (TensorFlo [()] 0 tf_op_layer_strided_slice_1203[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_361 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_841[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_361 (TensorFlo [()] 0 tf_op_layer_strided_slice_1206[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_362 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_842[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_362 (TensorFlo [()] 0 tf_op_layer_strided_slice_1209[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_360 (Tensor [(None, None)] 0 tf_op_layer_Neg_360[0][0]
tf_op_layer_Cast_360[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_361 (Tensor [(None, None)] 0 tf_op_layer_Neg_361[0][0]
tf_op_layer_Cast_361[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_362 (Tensor [(None, None)] 0 tf_op_layer_Neg_362[0][0]
tf_op_layer_Cast_362[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_360 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_360[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_361 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_361[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_362 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_362[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_843 (TensorFlo [()] 0 tf_op_layer_Exp_360[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_844 (TensorFlo [()] 0 tf_op_layer_Exp_361[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_845 (TensorFlo [()] 0 tf_op_layer_Exp_362[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_604 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_240 (TensorFl [()] 0 tf_op_layer_Mean_843[0][0]
tf_op_layer_Mean_844[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_361 (TensorFlow [()] 0 tf_op_layer_Mean_845[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_483 (TensorF [(None, 97)] 0 tf_op_layer_Sub_604[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_603 (TensorFlow [()] 0 tf_op_layer_AddV2_240[0][0]
tf_op_layer_Mul_361[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_120 (TensorFlow [(None,)] 0 tf_op_layer_Square_483[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_362 (TensorFlow [()] 0 tf_op_layer_Sub_603[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_241 (TensorFl [(None,)] 0 tf_op_layer_Sum_120[0][0]
tf_op_layer_Mul_362[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_846 (TensorFlo [()] 0 tf_op_layer_AddV2_241[0][0]
__________________________________________________________________________________________________
add_loss_120 (AddLoss) () 0 tf_op_layer_Mean_846[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 8.2141WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.0978s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9507 - val_loss: 0.9827
Epoch 2/100
218/218 [==============================] - 0s 916us/step - loss: 0.8517 - val_loss: 0.7719
Epoch 3/100
218/218 [==============================] - 0s 926us/step - loss: 0.7029 - val_loss: 0.6797
Epoch 4/100
218/218 [==============================] - 0s 922us/step - loss: 0.6396 - val_loss: 0.6400
Epoch 5/100
218/218 [==============================] - 0s 920us/step - loss: 0.6063 - val_loss: 0.6094
Epoch 6/100
218/218 [==============================] - 0s 924us/step - loss: 0.5879 - val_loss: 0.5865
Epoch 7/100
218/218 [==============================] - 0s 922us/step - loss: 0.5693 - val_loss: 0.5685
Epoch 8/100
218/218 [==============================] - 0s 923us/step - loss: 0.5572 - val_loss: 0.5654
Epoch 9/100
218/218 [==============================] - 0s 923us/step - loss: 0.5451 - val_loss: 0.5527
Epoch 10/100
218/218 [==============================] - 0s 921us/step - loss: 0.5394 - val_loss: 0.5462
Epoch 11/100
218/218 [==============================] - 0s 921us/step - loss: 0.5320 - val_loss: 0.5358
Epoch 12/100
218/218 [==============================] - 0s 931us/step - loss: 0.5242 - val_loss: 0.5408
Epoch 13/100
218/218 [==============================] - 0s 936us/step - loss: 0.5203 - val_loss: 0.5290
Epoch 14/100
218/218 [==============================] - 0s 918us/step - loss: 0.5183 - val_loss: 0.5237
Epoch 15/100
218/218 [==============================] - 0s 925us/step - loss: 0.5127 - val_loss: 0.5226
Epoch 16/100
218/218 [==============================] - 0s 928us/step - loss: 0.5071 - val_loss: 0.5145
Epoch 17/100
218/218 [==============================] - 0s 931us/step - loss: 0.5032 - val_loss: 0.5133
Epoch 18/100
218/218 [==============================] - 0s 929us/step - loss: 0.5027 - val_loss: 0.5080
Epoch 19/100
218/218 [==============================] - 0s 919us/step - loss: 0.5005 - val_loss: 0.5073
Epoch 20/100
218/218 [==============================] - 0s 926us/step - loss: 0.4986 - val_loss: 0.5038
Epoch 21/100
218/218 [==============================] - 0s 917us/step - loss: 0.4948 - val_loss: 0.5034
Epoch 22/100
218/218 [==============================] - 0s 931us/step - loss: 0.4933 - val_loss: 0.5015
Epoch 23/100
218/218 [==============================] - 0s 940us/step - loss: 0.4914 - val_loss: 0.5001
Epoch 24/100
218/218 [==============================] - 0s 922us/step - loss: 0.4886 - val_loss: 0.5006
Epoch 25/100
218/218 [==============================] - 0s 920us/step - loss: 0.4883 - val_loss: 0.4955
Epoch 26/100
218/218 [==============================] - 0s 927us/step - loss: 0.4851 - val_loss: 0.4952
Epoch 27/100
218/218 [==============================] - 0s 921us/step - loss: 0.4818 - val_loss: 0.4981
Epoch 28/100
218/218 [==============================] - 0s 922us/step - loss: 0.4862 - val_loss: 0.4928
Epoch 29/100
218/218 [==============================] - 0s 919us/step - loss: 0.4813 - val_loss: 0.4902
Epoch 30/100
218/218 [==============================] - 0s 924us/step - loss: 0.4809 - val_loss: 0.4897
Epoch 31/100
218/218 [==============================] - 0s 920us/step - loss: 0.4808 - val_loss: 0.4862
Epoch 32/100
218/218 [==============================] - 0s 922us/step - loss: 0.4784 - val_loss: 0.4891
Epoch 33/100
218/218 [==============================] - 0s 927us/step - loss: 0.4766 - val_loss: 0.4864
Epoch 34/100
218/218 [==============================] - 0s 926us/step - loss: 0.4805 - val_loss: 0.4852
Epoch 35/100
218/218 [==============================] - 0s 908us/step - loss: 0.4760 - val_loss: 0.4838
Epoch 36/100
218/218 [==============================] - 0s 930us/step - loss: 0.4744 - val_loss: 0.4870
Epoch 37/100
218/218 [==============================] - 0s 925us/step - loss: 0.4773 - val_loss: 0.4875
Epoch 38/100
218/218 [==============================] - 0s 927us/step - loss: 0.4756 - val_loss: 0.4859
Epoch 39/100
218/218 [==============================] - 0s 919us/step - loss: 0.4754 - val_loss: 0.4806
Epoch 40/100
218/218 [==============================] - 0s 930us/step - loss: 0.4731 - val_loss: 0.4810
Epoch 41/100
218/218 [==============================] - 0s 936us/step - loss: 0.4727 - val_loss: 0.4834
Epoch 42/100
218/218 [==============================] - 0s 927us/step - loss: 0.4749 - val_loss: 0.4772
Epoch 43/100
218/218 [==============================] - 0s 919us/step - loss: 0.4724 - val_loss: 0.4825
Epoch 44/100
218/218 [==============================] - 0s 917us/step - loss: 0.4696 - val_loss: 0.4796
Epoch 45/100
218/218 [==============================] - 0s 919us/step - loss: 0.4700 - val_loss: 0.4782
Epoch 46/100
218/218 [==============================] - 0s 924us/step - loss: 0.4699 - val_loss: 0.4798
Epoch 47/100
218/218 [==============================] - 0s 937us/step - loss: 0.4688 - val_loss: 0.4773
Epoch 48/100
218/218 [==============================] - 0s 930us/step - loss: 0.4695 - val_loss: 0.4764
Epoch 49/100
218/218 [==============================] - 0s 920us/step - loss: 0.4692 - val_loss: 0.4769
Epoch 50/100
218/218 [==============================] - 0s 918us/step - loss: 0.4693 - val_loss: 0.4761
Epoch 51/100
218/218 [==============================] - 0s 919us/step - loss: 0.4678 - val_loss: 0.4727
Epoch 52/100
218/218 [==============================] - 0s 922us/step - loss: 0.4653 - val_loss: 0.4746
Epoch 53/100
218/218 [==============================] - 0s 921us/step - loss: 0.4681 - val_loss: 0.4739
Epoch 54/100
218/218 [==============================] - 0s 918us/step - loss: 0.4664 - val_loss: 0.4774
Epoch 55/100
218/218 [==============================] - 0s 927us/step - loss: 0.4666 - val_loss: 0.4764
Epoch 56/100
218/218 [==============================] - 0s 920us/step - loss: 0.4664 - val_loss: 0.4744
Epoch 57/100
218/218 [==============================] - 0s 928us/step - loss: 0.4659 - val_loss: 0.4771
Epoch 58/100
218/218 [==============================] - 0s 929us/step - loss: 0.4675 - val_loss: 0.4733
Epoch 59/100
218/218 [==============================] - 0s 944us/step - loss: 0.4658 - val_loss: 0.4713
Epoch 60/100
218/218 [==============================] - 0s 922us/step - loss: 0.4644 - val_loss: 0.4735
Epoch 61/100
218/218 [==============================] - 0s 918us/step - loss: 0.4659 - val_loss: 0.4682
Epoch 62/100
218/218 [==============================] - 0s 928us/step - loss: 0.4641 - val_loss: 0.4753
Epoch 63/100
218/218 [==============================] - 0s 921us/step - loss: 0.4643 - val_loss: 0.4708
Epoch 64/100
218/218 [==============================] - 0s 931us/step - loss: 0.4648 - val_loss: 0.4738
Epoch 65/100
218/218 [==============================] - 0s 929us/step - loss: 0.4631 - val_loss: 0.4737
Epoch 66/100
218/218 [==============================] - 0s 928us/step - loss: 0.4637 - val_loss: 0.4729
Epoch 67/100
218/218 [==============================] - 0s 926us/step - loss: 0.4617 - val_loss: 0.4699
Epoch 68/100
218/218 [==============================] - 0s 916us/step - loss: 0.4627 - val_loss: 0.4708
Epoch 69/100
218/218 [==============================] - 0s 921us/step - loss: 0.4646 - val_loss: 0.4691
Epoch 70/100
218/218 [==============================] - 0s 918us/step - loss: 0.4643 - val_loss: 0.4704
Epoch 71/100
218/218 [==============================] - 0s 920us/step - loss: 0.4635 - val_loss: 0.4679
Epoch 72/100
218/218 [==============================] - 0s 925us/step - loss: 0.4620 - val_loss: 0.4681
Epoch 73/100
218/218 [==============================] - 0s 930us/step - loss: 0.4620 - val_loss: 0.4703
Epoch 74/100
218/218 [==============================] - 0s 932us/step - loss: 0.4613 - val_loss: 0.4636
Epoch 75/100
218/218 [==============================] - 0s 928us/step - loss: 0.4595 - val_loss: 0.4698
Epoch 76/100
218/218 [==============================] - 0s 921us/step - loss: 0.4625 - val_loss: 0.4700
Epoch 77/100
218/218 [==============================] - 0s 932us/step - loss: 0.4598 - val_loss: 0.4722
Epoch 78/100
218/218 [==============================] - 0s 922us/step - loss: 0.4593 - val_loss: 0.4695
Epoch 79/100
218/218 [==============================] - 0s 928us/step - loss: 0.4622 - val_loss: 0.4698
Epoch 80/100
218/218 [==============================] - 0s 926us/step - loss: 0.4601 - val_loss: 0.4695
Epoch 81/100
218/218 [==============================] - 0s 933us/step - loss: 0.4607 - val_loss: 0.4669
Epoch 82/100
218/218 [==============================] - 0s 927us/step - loss: 0.4591 - val_loss: 0.4691
Epoch 83/100
218/218 [==============================] - 0s 921us/step - loss: 0.4594 - val_loss: 0.4670
Epoch 84/100
218/218 [==============================] - 0s 917us/step - loss: 0.4586 - val_loss: 0.4669
Epoch 85/100
218/218 [==============================] - 0s 923us/step - loss: 0.4593 - val_loss: 0.4713
Epoch 86/100
218/218 [==============================] - 0s 942us/step - loss: 0.4602 - val_loss: 0.4690
Epoch 87/100
218/218 [==============================] - 0s 951us/step - loss: 0.4604 - val_loss: 0.4680
Epoch 88/100
218/218 [==============================] - 0s 955us/step - loss: 0.4585 - val_loss: 0.4656
Epoch 89/100
218/218 [==============================] - 0s 951us/step - loss: 0.4615 - val_loss: 0.4659
Epoch 90/100
218/218 [==============================] - 0s 953us/step - loss: 0.4580 - val_loss: 0.4659
Epoch 91/100
218/218 [==============================] - 0s 961us/step - loss: 0.4592 - val_loss: 0.4651
Epoch 92/100
218/218 [==============================] - 0s 958us/step - loss: 0.4596 - val_loss: 0.4647
Epoch 93/100
218/218 [==============================] - 0s 962us/step - loss: 0.4573 - val_loss: 0.4660
Epoch 94/100
218/218 [==============================] - 0s 956us/step - loss: 0.4586 - val_loss: 0.4622
Epoch 95/100
218/218 [==============================] - 0s 950us/step - loss: 0.4580 - val_loss: 0.4663
Epoch 96/100
218/218 [==============================] - 0s 961us/step - loss: 0.4569 - val_loss: 0.4653
Epoch 97/100
218/218 [==============================] - 0s 949us/step - loss: 0.4576 - val_loss: 0.4644
Epoch 98/100
218/218 [==============================] - 0s 954us/step - loss: 0.4590 - val_loss: 0.4676
Epoch 99/100
218/218 [==============================] - 0s 949us/step - loss: 0.4583 - val_loss: 0.4664
Epoch 100/100
218/218 [==============================] - 0s 951us/step - loss: 0.4585 - val_loss: 0.4665
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.13 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.39 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.86 seconds.
Calculating KMeans...
Calculated KMeans in 22.29 seconds.
Calculated landmark operator in 25.45 seconds.
Calculating optimal t...
Automatically selected t = 32
Calculated optimal t in 5.31 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.49 seconds.
Calculating metric MDS...
Calculated metric MDS in 19.00 seconds.
Calculated PHATE in 63.65 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_365 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_366 (Dense) (None, 32) 2080 dense_365[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_366[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_366[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_367 (Dense) (None, 32) 128
_________________________________________________________________
dense_368 (Dense) (None, 64) 2112
_________________________________________________________________
dense_369 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_365 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_366 (Dense) (None, 32) 2080 dense_365[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_366[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_366[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1210 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1210 [()] 0 tf_op_layer_Shape_1210[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_121 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1210[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_121[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_363 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_121 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_363[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1211 (TensorF [(2,)] 0 tf_op_layer_Add_121[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1213 (TensorF [(2,)] 0 tf_op_layer_Add_121[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1212 (TensorF [(2,)] 0 tf_op_layer_Add_121[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1214 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1216 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1215 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1217 (TensorF [(2,)] 0 tf_op_layer_Add_121[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1219 (TensorF [(2,)] 0 tf_op_layer_Add_121[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1218 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1211 [()] 0 tf_op_layer_Shape_1211[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1213 [()] 0 tf_op_layer_Shape_1213[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1212 [()] 0 tf_op_layer_Shape_1212[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1214 [()] 0 tf_op_layer_Shape_1214[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1216 [()] 0 tf_op_layer_Shape_1216[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1215 [()] 0 tf_op_layer_Shape_1215[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1217 [()] 0 tf_op_layer_Shape_1217[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1219 [()] 0 tf_op_layer_Shape_1219[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1218 [()] 0 tf_op_layer_Shape_1218[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_726/shape ( [(3,)] 0 tf_op_layer_strided_slice_1211[0]
tf_op_layer_strided_slice_1213[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_727/shape ( [(3,)] 0 tf_op_layer_strided_slice_1212[0]
tf_op_layer_strided_slice_1213[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_728/shape ( [(3,)] 0 tf_op_layer_strided_slice_1214[0]
tf_op_layer_strided_slice_1216[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_729/shape ( [(3,)] 0 tf_op_layer_strided_slice_1215[0]
tf_op_layer_strided_slice_1216[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_730/shape ( [(3,)] 0 tf_op_layer_strided_slice_1217[0]
tf_op_layer_strided_slice_1219[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_731/shape ( [(3,)] 0 tf_op_layer_strided_slice_1218[0]
tf_op_layer_strided_slice_1219[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_726 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_121[0][0]
tf_op_layer_Reshape_726/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_726/multiples [(3,)] 0 tf_op_layer_strided_slice_1212[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_727 (Tensor [(1, None, None)] 0 tf_op_layer_Add_121[0][0]
tf_op_layer_Reshape_727/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_727/multiples [(3,)] 0 tf_op_layer_strided_slice_1211[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_728 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_728/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_728/multiples [(3,)] 0 tf_op_layer_strided_slice_1215[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_729 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_729/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_729/multiples [(3,)] 0 tf_op_layer_strided_slice_1214[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_730 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_121[0][0]
tf_op_layer_Reshape_730/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_730/multiples [(3,)] 0 tf_op_layer_strided_slice_1218[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_731 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_731/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_731/multiples [(3,)] 0 tf_op_layer_strided_slice_1217[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_726 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_726[0][0]
tf_op_layer_Tile_726/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_727 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_727[0][0]
tf_op_layer_Tile_727/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_728 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_728[0][0]
tf_op_layer_Tile_728/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_729 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_729[0][0]
tf_op_layer_Tile_729/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_730 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_730[0][0]
tf_op_layer_Tile_730/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_731 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_731[0][0]
tf_op_layer_Tile_731/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_605 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_726[0][0]
tf_op_layer_Tile_727[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_606 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_728[0][0]
tf_op_layer_Tile_729[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_607 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_730[0][0]
tf_op_layer_Tile_731[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_484 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_605[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_485 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_606[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_486 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_607[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_847 (TensorFlo [(None, None)] 0 tf_op_layer_Square_484[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_848 (TensorFlo [(None, None)] 0 tf_op_layer_Square_485[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_849 (TensorFlo [(None, None)] 0 tf_op_layer_Square_486[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_363 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_847[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_363 (TensorFlo [()] 0 tf_op_layer_strided_slice_1213[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_364 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_848[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_364 (TensorFlo [()] 0 tf_op_layer_strided_slice_1216[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_365 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_849[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_365 (TensorFlo [()] 0 tf_op_layer_strided_slice_1219[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_363 (Tensor [(None, None)] 0 tf_op_layer_Neg_363[0][0]
tf_op_layer_Cast_363[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_364 (Tensor [(None, None)] 0 tf_op_layer_Neg_364[0][0]
tf_op_layer_Cast_364[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_365 (Tensor [(None, None)] 0 tf_op_layer_Neg_365[0][0]
tf_op_layer_Cast_365[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_363 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_363[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_364 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_364[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_365 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_365[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_850 (TensorFlo [()] 0 tf_op_layer_Exp_363[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_851 (TensorFlo [()] 0 tf_op_layer_Exp_364[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_852 (TensorFlo [()] 0 tf_op_layer_Exp_365[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_609 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_242 (TensorFl [()] 0 tf_op_layer_Mean_850[0][0]
tf_op_layer_Mean_851[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_364 (TensorFlow [()] 0 tf_op_layer_Mean_852[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_487 (TensorF [(None, 97)] 0 tf_op_layer_Sub_609[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_608 (TensorFlow [()] 0 tf_op_layer_AddV2_242[0][0]
tf_op_layer_Mul_364[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_121 (TensorFlow [(None,)] 0 tf_op_layer_Square_487[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_365 (TensorFlow [()] 0 tf_op_layer_Sub_608[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_243 (TensorFl [(None,)] 0 tf_op_layer_Sum_121[0][0]
tf_op_layer_Mul_365[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_853 (TensorFlo [()] 0 tf_op_layer_AddV2_243[0][0]
__________________________________________________________________________________________________
add_loss_121 (AddLoss) () 0 tf_op_layer_Mean_853[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 7.9041WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.1106s). Check your callbacks.
218/218 [==============================] - 1s 3ms/step - loss: 0.8447 - val_loss: 0.4698
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4263 - val_loss: 0.3777
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3909 - val_loss: 0.3739
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3810 - val_loss: 0.3619
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3747 - val_loss: 0.3570
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3710 - val_loss: 0.3567
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3703 - val_loss: 0.3525
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3669 - val_loss: 0.3535
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3661 - val_loss: 0.3495
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3633 - val_loss: 0.3487
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3612 - val_loss: 0.3449
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3616 - val_loss: 0.3468
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3586 - val_loss: 0.3462
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3590 - val_loss: 0.3440
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3583 - val_loss: 0.3414
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3573 - val_loss: 0.3403
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3572 - val_loss: 0.3442
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3554 - val_loss: 0.3452
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3552 - val_loss: 0.3396
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3560 - val_loss: 0.3402
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3553 - val_loss: 0.3398
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3545 - val_loss: 0.3393
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3541 - val_loss: 0.3408
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3394
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3531 - val_loss: 0.3399
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3545 - val_loss: 0.3433
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3530 - val_loss: 0.3397
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3515 - val_loss: 0.3374
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3525 - val_loss: 0.3397
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3521 - val_loss: 0.3378
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3500 - val_loss: 0.3379
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3517 - val_loss: 0.3394
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3501 - val_loss: 0.3394
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3405
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3349
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3390
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3521 - val_loss: 0.3394
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3371
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3367
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3344
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3370
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3498 - val_loss: 0.3363
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3484 - val_loss: 0.3358
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3368
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3348
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3341
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3492 - val_loss: 0.3349
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3370
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3479 - val_loss: 0.3345
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3483 - val_loss: 0.3369
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3356
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3369
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3344
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3365
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3314
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3349
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3335
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3474 - val_loss: 0.3363
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3345
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3346
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3337
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3317
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3329
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3347
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3339
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3318
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3331
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3345
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3355
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3330
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3455 - val_loss: 0.3335
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3295
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3331
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3350
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3336
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3319
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3328
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3327
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3316
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3310
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3336
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3322
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3304
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3320
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3332
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3327
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3317
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3320
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3336
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3319
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3341
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3304
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3332
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3326
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3339
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3298
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3287
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3301
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3320
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3307
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_370 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1220 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1220 [()] 0 tf_op_layer_Shape_1220[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_122 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1220[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_122[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_366 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_122 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_366[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1221 (TensorF [(2,)] 0 tf_op_layer_Add_122[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1223 (TensorF [(2,)] 0 tf_op_layer_Add_122[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1222 (TensorF [(2,)] 0 tf_op_layer_Add_122[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1224 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1226 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1225 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1227 (TensorF [(2,)] 0 tf_op_layer_Add_122[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1229 (TensorF [(2,)] 0 tf_op_layer_Add_122[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1228 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1221 [()] 0 tf_op_layer_Shape_1221[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1223 [()] 0 tf_op_layer_Shape_1223[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1222 [()] 0 tf_op_layer_Shape_1222[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1224 [()] 0 tf_op_layer_Shape_1224[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1226 [()] 0 tf_op_layer_Shape_1226[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1225 [()] 0 tf_op_layer_Shape_1225[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1227 [()] 0 tf_op_layer_Shape_1227[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1229 [()] 0 tf_op_layer_Shape_1229[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1228 [()] 0 tf_op_layer_Shape_1228[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_732/shape ( [(3,)] 0 tf_op_layer_strided_slice_1221[0]
tf_op_layer_strided_slice_1223[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_733/shape ( [(3,)] 0 tf_op_layer_strided_slice_1222[0]
tf_op_layer_strided_slice_1223[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_734/shape ( [(3,)] 0 tf_op_layer_strided_slice_1224[0]
tf_op_layer_strided_slice_1226[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_735/shape ( [(3,)] 0 tf_op_layer_strided_slice_1225[0]
tf_op_layer_strided_slice_1226[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_736/shape ( [(3,)] 0 tf_op_layer_strided_slice_1227[0]
tf_op_layer_strided_slice_1229[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_737/shape ( [(3,)] 0 tf_op_layer_strided_slice_1228[0]
tf_op_layer_strided_slice_1229[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_732 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_122[0][0]
tf_op_layer_Reshape_732/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_732/multiples [(3,)] 0 tf_op_layer_strided_slice_1222[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_733 (Tensor [(1, None, None)] 0 tf_op_layer_Add_122[0][0]
tf_op_layer_Reshape_733/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_733/multiples [(3,)] 0 tf_op_layer_strided_slice_1221[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_734 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_734/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_734/multiples [(3,)] 0 tf_op_layer_strided_slice_1225[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_735 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_735/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_735/multiples [(3,)] 0 tf_op_layer_strided_slice_1224[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_736 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_122[0][0]
tf_op_layer_Reshape_736/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_736/multiples [(3,)] 0 tf_op_layer_strided_slice_1228[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_737 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_737/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_737/multiples [(3,)] 0 tf_op_layer_strided_slice_1227[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_732 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_732[0][0]
tf_op_layer_Tile_732/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_733 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_733[0][0]
tf_op_layer_Tile_733/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_734 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_734[0][0]
tf_op_layer_Tile_734/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_735 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_735[0][0]
tf_op_layer_Tile_735/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_736 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_736[0][0]
tf_op_layer_Tile_736/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_737 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_737[0][0]
tf_op_layer_Tile_737/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_610 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_732[0][0]
tf_op_layer_Tile_733[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_611 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_734[0][0]
tf_op_layer_Tile_735[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_612 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_736[0][0]
tf_op_layer_Tile_737[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_488 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_610[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_489 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_611[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_490 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_612[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_854 (TensorFlo [(None, None)] 0 tf_op_layer_Square_488[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_855 (TensorFlo [(None, None)] 0 tf_op_layer_Square_489[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_856 (TensorFlo [(None, None)] 0 tf_op_layer_Square_490[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_366 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_854[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_366 (TensorFlo [()] 0 tf_op_layer_strided_slice_1223[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_367 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_855[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_367 (TensorFlo [()] 0 tf_op_layer_strided_slice_1226[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_368 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_856[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_368 (TensorFlo [()] 0 tf_op_layer_strided_slice_1229[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_366 (Tensor [(None, None)] 0 tf_op_layer_Neg_366[0][0]
tf_op_layer_Cast_366[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_367 (Tensor [(None, None)] 0 tf_op_layer_Neg_367[0][0]
tf_op_layer_Cast_367[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_368 (Tensor [(None, None)] 0 tf_op_layer_Neg_368[0][0]
tf_op_layer_Cast_368[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_366 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_366[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_367 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_367[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_368 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_368[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_857 (TensorFlo [()] 0 tf_op_layer_Exp_366[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_858 (TensorFlo [()] 0 tf_op_layer_Exp_367[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_859 (TensorFlo [()] 0 tf_op_layer_Exp_368[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_614 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_244 (TensorFl [()] 0 tf_op_layer_Mean_857[0][0]
tf_op_layer_Mean_858[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_367 (TensorFlow [()] 0 tf_op_layer_Mean_859[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_491 (TensorF [(None, 97)] 0 tf_op_layer_Sub_614[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_613 (TensorFlow [()] 0 tf_op_layer_AddV2_244[0][0]
tf_op_layer_Mul_367[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_122 (TensorFlow [(None,)] 0 tf_op_layer_Square_491[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_368 (TensorFlow [()] 0 tf_op_layer_Sub_613[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_245 (TensorFl [(None,)] 0 tf_op_layer_Sum_122[0][0]
tf_op_layer_Mul_368[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_860 (TensorFlo [()] 0 tf_op_layer_AddV2_245[0][0]
__________________________________________________________________________________________________
add_loss_122 (AddLoss) () 0 tf_op_layer_Mean_860[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 9s - loss: 8.3457WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.0878s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.8740 - val_loss: 0.9793
Epoch 2/100
218/218 [==============================] - 0s 854us/step - loss: 0.8477 - val_loss: 0.7805
Epoch 3/100
218/218 [==============================] - 0s 853us/step - loss: 0.7193 - val_loss: 0.6890
Epoch 4/100
218/218 [==============================] - 0s 887us/step - loss: 0.6454 - val_loss: 0.6248
Epoch 5/100
218/218 [==============================] - 0s 870us/step - loss: 0.6046 - val_loss: 0.6003
Epoch 6/100
218/218 [==============================] - 0s 907us/step - loss: 0.5789 - val_loss: 0.5793
Epoch 7/100
218/218 [==============================] - 0s 900us/step - loss: 0.5635 - val_loss: 0.5736
Epoch 8/100
218/218 [==============================] - 0s 979us/step - loss: 0.5515 - val_loss: 0.5618
Epoch 9/100
218/218 [==============================] - 0s 981us/step - loss: 0.5464 - val_loss: 0.5591
Epoch 10/100
218/218 [==============================] - 0s 896us/step - loss: 0.5383 - val_loss: 0.5487
Epoch 11/100
218/218 [==============================] - 0s 938us/step - loss: 0.5284 - val_loss: 0.5389
Epoch 12/100
218/218 [==============================] - 0s 907us/step - loss: 0.5261 - val_loss: 0.5312
Epoch 13/100
218/218 [==============================] - 0s 889us/step - loss: 0.5221 - val_loss: 0.5284
Epoch 14/100
218/218 [==============================] - 0s 919us/step - loss: 0.5167 - val_loss: 0.5250
Epoch 15/100
218/218 [==============================] - 0s 886us/step - loss: 0.5135 - val_loss: 0.5199
Epoch 16/100
218/218 [==============================] - 0s 900us/step - loss: 0.5078 - val_loss: 0.5119
Epoch 17/100
218/218 [==============================] - 0s 906us/step - loss: 0.5083 - val_loss: 0.5190
Epoch 18/100
218/218 [==============================] - 0s 923us/step - loss: 0.5046 - val_loss: 0.5097
Epoch 19/100
218/218 [==============================] - 0s 882us/step - loss: 0.5039 - val_loss: 0.5132
Epoch 20/100
218/218 [==============================] - 0s 929us/step - loss: 0.4987 - val_loss: 0.5198
Epoch 21/100
218/218 [==============================] - 0s 892us/step - loss: 0.4961 - val_loss: 0.5098
Epoch 22/100
218/218 [==============================] - 0s 922us/step - loss: 0.4975 - val_loss: 0.5074
Epoch 23/100
218/218 [==============================] - 0s 931us/step - loss: 0.4963 - val_loss: 0.5087
Epoch 24/100
218/218 [==============================] - 0s 946us/step - loss: 0.4943 - val_loss: 0.5034
Epoch 25/100
218/218 [==============================] - 0s 936us/step - loss: 0.4916 - val_loss: 0.5027
Epoch 26/100
218/218 [==============================] - 0s 895us/step - loss: 0.4933 - val_loss: 0.5029
Epoch 27/100
218/218 [==============================] - 0s 904us/step - loss: 0.4889 - val_loss: 0.4949
Epoch 28/100
218/218 [==============================] - 0s 945us/step - loss: 0.4859 - val_loss: 0.4956
Epoch 29/100
218/218 [==============================] - 0s 940us/step - loss: 0.4860 - val_loss: 0.4966
Epoch 30/100
218/218 [==============================] - 0s 924us/step - loss: 0.4849 - val_loss: 0.4983
Epoch 31/100
218/218 [==============================] - 0s 917us/step - loss: 0.4808 - val_loss: 0.4887
Epoch 32/100
218/218 [==============================] - 0s 935us/step - loss: 0.4840 - val_loss: 0.4951
Epoch 33/100
218/218 [==============================] - 0s 980us/step - loss: 0.4831 - val_loss: 0.4972
Epoch 34/100
218/218 [==============================] - 0s 994us/step - loss: 0.4818 - val_loss: 0.4931
Epoch 35/100
218/218 [==============================] - 0s 961us/step - loss: 0.4808 - val_loss: 0.4964
Epoch 36/100
218/218 [==============================] - 0s 969us/step - loss: 0.4778 - val_loss: 0.4908
Epoch 37/100
218/218 [==============================] - 0s 979us/step - loss: 0.4809 - val_loss: 0.4909
Epoch 38/100
218/218 [==============================] - 0s 997us/step - loss: 0.4796 - val_loss: 0.4884
Epoch 39/100
218/218 [==============================] - 0s 993us/step - loss: 0.4806 - val_loss: 0.4890
Epoch 40/100
218/218 [==============================] - 0s 991us/step - loss: 0.4770 - val_loss: 0.4947
Epoch 41/100
218/218 [==============================] - 0s 978us/step - loss: 0.4763 - val_loss: 0.4915
Epoch 42/100
218/218 [==============================] - 0s 956us/step - loss: 0.4771 - val_loss: 0.4859
Epoch 43/100
218/218 [==============================] - 0s 968us/step - loss: 0.4769 - val_loss: 0.4898
Epoch 44/100
218/218 [==============================] - 0s 995us/step - loss: 0.4748 - val_loss: 0.4871
Epoch 45/100
218/218 [==============================] - 0s 967us/step - loss: 0.4747 - val_loss: 0.4862
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4746 - val_loss: 0.4900
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4745 - val_loss: 0.4894
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4705 - val_loss: 0.4818
Epoch 49/100
218/218 [==============================] - 0s 963us/step - loss: 0.4731 - val_loss: 0.4863
Epoch 50/100
218/218 [==============================] - 0s 980us/step - loss: 0.4730 - val_loss: 0.4800
Epoch 51/100
218/218 [==============================] - 0s 979us/step - loss: 0.4717 - val_loss: 0.4835
Epoch 52/100
218/218 [==============================] - 0s 974us/step - loss: 0.4696 - val_loss: 0.4832
Epoch 53/100
218/218 [==============================] - 0s 977us/step - loss: 0.4719 - val_loss: 0.4865
Epoch 54/100
218/218 [==============================] - 0s 979us/step - loss: 0.4715 - val_loss: 0.4818
Epoch 55/100
218/218 [==============================] - 0s 958us/step - loss: 0.4706 - val_loss: 0.4820
Epoch 56/100
218/218 [==============================] - 0s 985us/step - loss: 0.4703 - val_loss: 0.4818
Epoch 57/100
218/218 [==============================] - 0s 986us/step - loss: 0.4692 - val_loss: 0.4773
Epoch 58/100
218/218 [==============================] - 0s 947us/step - loss: 0.4702 - val_loss: 0.4817
Epoch 59/100
218/218 [==============================] - 0s 960us/step - loss: 0.4674 - val_loss: 0.4799
Epoch 60/100
218/218 [==============================] - 0s 939us/step - loss: 0.4706 - val_loss: 0.4804
Epoch 61/100
218/218 [==============================] - 0s 995us/step - loss: 0.4678 - val_loss: 0.4784
Epoch 62/100
218/218 [==============================] - 0s 996us/step - loss: 0.4695 - val_loss: 0.4803
Epoch 63/100
218/218 [==============================] - 0s 981us/step - loss: 0.4670 - val_loss: 0.4822
Epoch 64/100
218/218 [==============================] - 0s 997us/step - loss: 0.4674 - val_loss: 0.4774
Epoch 65/100
218/218 [==============================] - 0s 997us/step - loss: 0.4681 - val_loss: 0.4805
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4658 - val_loss: 0.4825
Epoch 67/100
218/218 [==============================] - 0s 979us/step - loss: 0.4658 - val_loss: 0.4772
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4673 - val_loss: 0.4807
Epoch 69/100
218/218 [==============================] - 0s 976us/step - loss: 0.4677 - val_loss: 0.4813
Epoch 70/100
218/218 [==============================] - 0s 990us/step - loss: 0.4679 - val_loss: 0.4742
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4665 - val_loss: 0.4753
Epoch 72/100
218/218 [==============================] - 0s 996us/step - loss: 0.4639 - val_loss: 0.4754
Epoch 73/100
218/218 [==============================] - 0s 974us/step - loss: 0.4649 - val_loss: 0.4766
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4657 - val_loss: 0.4777
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4653 - val_loss: 0.4777
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4652 - val_loss: 0.4777
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4647 - val_loss: 0.4771
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4625 - val_loss: 0.4734
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4656 - val_loss: 0.4748
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4629 - val_loss: 0.4789
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4639 - val_loss: 0.4719
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4633 - val_loss: 0.4750
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4635 - val_loss: 0.4765
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4636 - val_loss: 0.4785
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4634 - val_loss: 0.4725
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4619 - val_loss: 0.4760
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4636 - val_loss: 0.4712
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4638 - val_loss: 0.4754
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4647 - val_loss: 0.4771
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4633 - val_loss: 0.4776
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4621 - val_loss: 0.4768
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4627 - val_loss: 0.4716
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4632 - val_loss: 0.4760
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4613 - val_loss: 0.4709
Epoch 95/100
218/218 [==============================] - 0s 999us/step - loss: 0.4619 - val_loss: 0.4714
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4607 - val_loss: 0.4735
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4710
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4618 - val_loss: 0.4723
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4613 - val_loss: 0.4729
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4604 - val_loss: 0.4751
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.60 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.86 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.83 seconds.
Calculating KMeans...
Calculated KMeans in 14.74 seconds.
Calculated landmark operator in 16.92 seconds.
Calculating optimal t...
Automatically selected t = 25
Calculated optimal t in 2.35 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.48 seconds.
Calculating metric MDS...
Calculated metric MDS in 15.47 seconds.
Calculated PHATE in 49.10 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_371 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_372 (Dense) (None, 32) 2080 dense_371[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_372[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_372[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_373 (Dense) (None, 32) 128
_________________________________________________________________
dense_374 (Dense) (None, 64) 2112
_________________________________________________________________
dense_375 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_371 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_372 (Dense) (None, 32) 2080 dense_371[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_372[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_372[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1230 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1230 [()] 0 tf_op_layer_Shape_1230[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_123 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1230[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_123[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_369 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_123 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_369[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1231 (TensorF [(2,)] 0 tf_op_layer_Add_123[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1233 (TensorF [(2,)] 0 tf_op_layer_Add_123[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1232 (TensorF [(2,)] 0 tf_op_layer_Add_123[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1234 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1236 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1235 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1237 (TensorF [(2,)] 0 tf_op_layer_Add_123[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1239 (TensorF [(2,)] 0 tf_op_layer_Add_123[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1238 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1231 [()] 0 tf_op_layer_Shape_1231[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1233 [()] 0 tf_op_layer_Shape_1233[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1232 [()] 0 tf_op_layer_Shape_1232[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1234 [()] 0 tf_op_layer_Shape_1234[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1236 [()] 0 tf_op_layer_Shape_1236[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1235 [()] 0 tf_op_layer_Shape_1235[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1237 [()] 0 tf_op_layer_Shape_1237[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1239 [()] 0 tf_op_layer_Shape_1239[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1238 [()] 0 tf_op_layer_Shape_1238[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_738/shape ( [(3,)] 0 tf_op_layer_strided_slice_1231[0]
tf_op_layer_strided_slice_1233[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_739/shape ( [(3,)] 0 tf_op_layer_strided_slice_1232[0]
tf_op_layer_strided_slice_1233[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_740/shape ( [(3,)] 0 tf_op_layer_strided_slice_1234[0]
tf_op_layer_strided_slice_1236[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_741/shape ( [(3,)] 0 tf_op_layer_strided_slice_1235[0]
tf_op_layer_strided_slice_1236[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_742/shape ( [(3,)] 0 tf_op_layer_strided_slice_1237[0]
tf_op_layer_strided_slice_1239[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_743/shape ( [(3,)] 0 tf_op_layer_strided_slice_1238[0]
tf_op_layer_strided_slice_1239[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_738 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_123[0][0]
tf_op_layer_Reshape_738/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_738/multiples [(3,)] 0 tf_op_layer_strided_slice_1232[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_739 (Tensor [(1, None, None)] 0 tf_op_layer_Add_123[0][0]
tf_op_layer_Reshape_739/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_739/multiples [(3,)] 0 tf_op_layer_strided_slice_1231[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_740 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_740/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_740/multiples [(3,)] 0 tf_op_layer_strided_slice_1235[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_741 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_741/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_741/multiples [(3,)] 0 tf_op_layer_strided_slice_1234[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_742 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_123[0][0]
tf_op_layer_Reshape_742/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_742/multiples [(3,)] 0 tf_op_layer_strided_slice_1238[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_743 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_743/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_743/multiples [(3,)] 0 tf_op_layer_strided_slice_1237[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_738 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_738[0][0]
tf_op_layer_Tile_738/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_739 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_739[0][0]
tf_op_layer_Tile_739/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_740 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_740[0][0]
tf_op_layer_Tile_740/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_741 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_741[0][0]
tf_op_layer_Tile_741/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_742 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_742[0][0]
tf_op_layer_Tile_742/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_743 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_743[0][0]
tf_op_layer_Tile_743/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_615 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_738[0][0]
tf_op_layer_Tile_739[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_616 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_740[0][0]
tf_op_layer_Tile_741[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_617 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_742[0][0]
tf_op_layer_Tile_743[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_492 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_615[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_493 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_616[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_494 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_617[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_861 (TensorFlo [(None, None)] 0 tf_op_layer_Square_492[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_862 (TensorFlo [(None, None)] 0 tf_op_layer_Square_493[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_863 (TensorFlo [(None, None)] 0 tf_op_layer_Square_494[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_369 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_861[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_369 (TensorFlo [()] 0 tf_op_layer_strided_slice_1233[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_370 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_862[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_370 (TensorFlo [()] 0 tf_op_layer_strided_slice_1236[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_371 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_863[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_371 (TensorFlo [()] 0 tf_op_layer_strided_slice_1239[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_369 (Tensor [(None, None)] 0 tf_op_layer_Neg_369[0][0]
tf_op_layer_Cast_369[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_370 (Tensor [(None, None)] 0 tf_op_layer_Neg_370[0][0]
tf_op_layer_Cast_370[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_371 (Tensor [(None, None)] 0 tf_op_layer_Neg_371[0][0]
tf_op_layer_Cast_371[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_369 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_369[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_370 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_370[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_371 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_371[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_864 (TensorFlo [()] 0 tf_op_layer_Exp_369[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_865 (TensorFlo [()] 0 tf_op_layer_Exp_370[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_866 (TensorFlo [()] 0 tf_op_layer_Exp_371[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_619 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_246 (TensorFl [()] 0 tf_op_layer_Mean_864[0][0]
tf_op_layer_Mean_865[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_370 (TensorFlow [()] 0 tf_op_layer_Mean_866[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_495 (TensorF [(None, 97)] 0 tf_op_layer_Sub_619[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_618 (TensorFlow [()] 0 tf_op_layer_AddV2_246[0][0]
tf_op_layer_Mul_370[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_123 (TensorFlow [(None,)] 0 tf_op_layer_Square_495[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_371 (TensorFlow [()] 0 tf_op_layer_Sub_618[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_247 (TensorFl [(None,)] 0 tf_op_layer_Sum_123[0][0]
tf_op_layer_Mul_371[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_867 (TensorFlo [()] 0 tf_op_layer_AddV2_247[0][0]
__________________________________________________________________________________________________
add_loss_123 (AddLoss) () 0 tf_op_layer_Mean_867[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 9s - loss: 7.7941WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.0891s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 0.9013 - val_loss: 0.4539
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4158 - val_loss: 0.3856
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3866 - val_loss: 0.3766
Epoch 4/100
218/218 [==============================] - 0s 997us/step - loss: 0.3788 - val_loss: 0.3663
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3714 - val_loss: 0.3647
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3688 - val_loss: 0.3588
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3673 - val_loss: 0.3568
Epoch 8/100
218/218 [==============================] - 0s 985us/step - loss: 0.3644 - val_loss: 0.3598
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3636 - val_loss: 0.3567
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3608 - val_loss: 0.3542
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3631 - val_loss: 0.3585
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3593 - val_loss: 0.3503
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3571 - val_loss: 0.3520
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3571 - val_loss: 0.3500
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3567 - val_loss: 0.3471
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3555 - val_loss: 0.3515
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3556 - val_loss: 0.3493
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3535 - val_loss: 0.3484
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3535 - val_loss: 0.3462
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3535 - val_loss: 0.3464
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3541 - val_loss: 0.3500
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3515
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3522 - val_loss: 0.3519
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3523 - val_loss: 0.3450
Epoch 25/100
218/218 [==============================] - 0s 998us/step - loss: 0.3522 - val_loss: 0.3464
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3458
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3451
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3504 - val_loss: 0.3451
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3441
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3450
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3421
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3498 - val_loss: 0.3439
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3502 - val_loss: 0.3430
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3492 - val_loss: 0.3446
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3501 - val_loss: 0.3430
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3482 - val_loss: 0.3394
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3451
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3483 - val_loss: 0.3433
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3448
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3451
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3415
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3414
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3440
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3451
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3422
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3423
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3434
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3415
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3408
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3433
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3392
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3419
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3412
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3401
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3420
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3439
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3412
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3395
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3416
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3385
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3408
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3378
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3403
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3382
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3396
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3390
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3400
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3396
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3377
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3388
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3403
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3401
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3422
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3398
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3380
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3404
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3402
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3383
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3385
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3387
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3365
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3396
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3377
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3368
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3395
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3393
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3393
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3373
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3375
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3393
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3382
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3376
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3378
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3412
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3397
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3399
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3370
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3380
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3388
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3368
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_376 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1240 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1240 [()] 0 tf_op_layer_Shape_1240[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_124 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1240[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_124[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_372 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_124 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_372[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1241 (TensorF [(2,)] 0 tf_op_layer_Add_124[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1243 (TensorF [(2,)] 0 tf_op_layer_Add_124[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1242 (TensorF [(2,)] 0 tf_op_layer_Add_124[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1244 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1246 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1245 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1247 (TensorF [(2,)] 0 tf_op_layer_Add_124[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1249 (TensorF [(2,)] 0 tf_op_layer_Add_124[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1248 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1241 [()] 0 tf_op_layer_Shape_1241[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1243 [()] 0 tf_op_layer_Shape_1243[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1242 [()] 0 tf_op_layer_Shape_1242[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1244 [()] 0 tf_op_layer_Shape_1244[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1246 [()] 0 tf_op_layer_Shape_1246[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1245 [()] 0 tf_op_layer_Shape_1245[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1247 [()] 0 tf_op_layer_Shape_1247[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1249 [()] 0 tf_op_layer_Shape_1249[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1248 [()] 0 tf_op_layer_Shape_1248[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_744/shape ( [(3,)] 0 tf_op_layer_strided_slice_1241[0]
tf_op_layer_strided_slice_1243[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_745/shape ( [(3,)] 0 tf_op_layer_strided_slice_1242[0]
tf_op_layer_strided_slice_1243[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_746/shape ( [(3,)] 0 tf_op_layer_strided_slice_1244[0]
tf_op_layer_strided_slice_1246[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_747/shape ( [(3,)] 0 tf_op_layer_strided_slice_1245[0]
tf_op_layer_strided_slice_1246[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_748/shape ( [(3,)] 0 tf_op_layer_strided_slice_1247[0]
tf_op_layer_strided_slice_1249[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_749/shape ( [(3,)] 0 tf_op_layer_strided_slice_1248[0]
tf_op_layer_strided_slice_1249[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_744 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_124[0][0]
tf_op_layer_Reshape_744/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_744/multiples [(3,)] 0 tf_op_layer_strided_slice_1242[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_745 (Tensor [(1, None, None)] 0 tf_op_layer_Add_124[0][0]
tf_op_layer_Reshape_745/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_745/multiples [(3,)] 0 tf_op_layer_strided_slice_1241[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_746 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_746/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_746/multiples [(3,)] 0 tf_op_layer_strided_slice_1245[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_747 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_747/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_747/multiples [(3,)] 0 tf_op_layer_strided_slice_1244[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_748 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_124[0][0]
tf_op_layer_Reshape_748/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_748/multiples [(3,)] 0 tf_op_layer_strided_slice_1248[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_749 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_749/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_749/multiples [(3,)] 0 tf_op_layer_strided_slice_1247[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_744 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_744[0][0]
tf_op_layer_Tile_744/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_745 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_745[0][0]
tf_op_layer_Tile_745/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_746 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_746[0][0]
tf_op_layer_Tile_746/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_747 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_747[0][0]
tf_op_layer_Tile_747/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_748 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_748[0][0]
tf_op_layer_Tile_748/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_749 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_749[0][0]
tf_op_layer_Tile_749/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_620 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_744[0][0]
tf_op_layer_Tile_745[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_621 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_746[0][0]
tf_op_layer_Tile_747[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_622 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_748[0][0]
tf_op_layer_Tile_749[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_496 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_620[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_497 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_621[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_498 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_622[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_868 (TensorFlo [(None, None)] 0 tf_op_layer_Square_496[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_869 (TensorFlo [(None, None)] 0 tf_op_layer_Square_497[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_870 (TensorFlo [(None, None)] 0 tf_op_layer_Square_498[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_372 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_868[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_372 (TensorFlo [()] 0 tf_op_layer_strided_slice_1243[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_373 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_869[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_373 (TensorFlo [()] 0 tf_op_layer_strided_slice_1246[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_374 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_870[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_374 (TensorFlo [()] 0 tf_op_layer_strided_slice_1249[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_372 (Tensor [(None, None)] 0 tf_op_layer_Neg_372[0][0]
tf_op_layer_Cast_372[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_373 (Tensor [(None, None)] 0 tf_op_layer_Neg_373[0][0]
tf_op_layer_Cast_373[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_374 (Tensor [(None, None)] 0 tf_op_layer_Neg_374[0][0]
tf_op_layer_Cast_374[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_372 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_372[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_373 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_373[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_374 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_374[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_871 (TensorFlo [()] 0 tf_op_layer_Exp_372[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_872 (TensorFlo [()] 0 tf_op_layer_Exp_373[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_873 (TensorFlo [()] 0 tf_op_layer_Exp_374[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_624 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_248 (TensorFl [()] 0 tf_op_layer_Mean_871[0][0]
tf_op_layer_Mean_872[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_373 (TensorFlow [()] 0 tf_op_layer_Mean_873[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_499 (TensorF [(None, 97)] 0 tf_op_layer_Sub_624[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_623 (TensorFlow [()] 0 tf_op_layer_AddV2_248[0][0]
tf_op_layer_Mul_373[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_124 (TensorFlow [(None,)] 0 tf_op_layer_Square_499[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_374 (TensorFlow [()] 0 tf_op_layer_Sub_623[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_249 (TensorFl [(None,)] 0 tf_op_layer_Sum_124[0][0]
tf_op_layer_Mul_374[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_874 (TensorFlo [()] 0 tf_op_layer_AddV2_249[0][0]
__________________________________________________________________________________________________
add_loss_124 (AddLoss) () 0 tf_op_layer_Mean_874[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.4382WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.0999s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 2.0035 - val_loss: 0.9552
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.8204 - val_loss: 0.7388
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6923 - val_loss: 0.6580
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6344 - val_loss: 0.6086
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6065 - val_loss: 0.5817
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5858 - val_loss: 0.5804
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5684 - val_loss: 0.5587
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5568 - val_loss: 0.5528
Epoch 9/100
218/218 [==============================] - 0s 983us/step - loss: 0.5482 - val_loss: 0.5384
Epoch 10/100
218/218 [==============================] - 0s 989us/step - loss: 0.5395 - val_loss: 0.5308
Epoch 11/100
218/218 [==============================] - 0s 991us/step - loss: 0.5296 - val_loss: 0.5336
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5301 - val_loss: 0.5252
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5228 - val_loss: 0.5201
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5190 - val_loss: 0.5088
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5172 - val_loss: 0.5092
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5113 - val_loss: 0.5068
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5081 - val_loss: 0.5054
Epoch 18/100
218/218 [==============================] - 0s 978us/step - loss: 0.5067 - val_loss: 0.5071
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5029 - val_loss: 0.4958
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5024 - val_loss: 0.5064
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5012 - val_loss: 0.4923
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4979 - val_loss: 0.4987
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4971 - val_loss: 0.4891
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4960 - val_loss: 0.4833
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4936 - val_loss: 0.4869
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4904 - val_loss: 0.4834
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4886 - val_loss: 0.4911
Epoch 28/100
218/218 [==============================] - 0s 971us/step - loss: 0.4885 - val_loss: 0.4834
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4881 - val_loss: 0.4848
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4856 - val_loss: 0.4856
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4836 - val_loss: 0.4796
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4833 - val_loss: 0.4852
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4844 - val_loss: 0.4808
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4822 - val_loss: 0.4764
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4801 - val_loss: 0.4792
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4790 - val_loss: 0.4766
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4807 - val_loss: 0.4767
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4796 - val_loss: 0.4769
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4793 - val_loss: 0.4737
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4773 - val_loss: 0.4749
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4780 - val_loss: 0.4753
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4777 - val_loss: 0.4722
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4764 - val_loss: 0.4738
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4756 - val_loss: 0.4694
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4752 - val_loss: 0.4700
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4749 - val_loss: 0.4724
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4760 - val_loss: 0.4750
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4719 - val_loss: 0.4742
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4722 - val_loss: 0.4667
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4715 - val_loss: 0.4770
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4714 - val_loss: 0.4719
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4728 - val_loss: 0.4710
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4716 - val_loss: 0.4697
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4698 - val_loss: 0.4651
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4708 - val_loss: 0.4678
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4698 - val_loss: 0.4695
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4704 - val_loss: 0.4644
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4683 - val_loss: 0.4664
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4682 - val_loss: 0.4705
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4701 - val_loss: 0.4622
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4693 - val_loss: 0.4623
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4673 - val_loss: 0.4622
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4685 - val_loss: 0.4699
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4678 - val_loss: 0.4617
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4683 - val_loss: 0.4632
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4684 - val_loss: 0.4622
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4654 - val_loss: 0.4635
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4665 - val_loss: 0.4622
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4686 - val_loss: 0.4637
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4668 - val_loss: 0.4676
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4665 - val_loss: 0.4626
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4647 - val_loss: 0.4617
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4666 - val_loss: 0.4643
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4646 - val_loss: 0.4648
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4646 - val_loss: 0.4584
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4661 - val_loss: 0.4642
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4657 - val_loss: 0.4564
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4665 - val_loss: 0.4621
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4647 - val_loss: 0.4595
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4651 - val_loss: 0.4631
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4639 - val_loss: 0.4575
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4628 - val_loss: 0.4665
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4626 - val_loss: 0.4590
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4635 - val_loss: 0.4573
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4651 - val_loss: 0.4606
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4641 - val_loss: 0.4594
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4634 - val_loss: 0.4559
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4622 - val_loss: 0.4631
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4637 - val_loss: 0.4609
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4625 - val_loss: 0.4566
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4635 - val_loss: 0.4581
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4629 - val_loss: 0.4612
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4606 - val_loss: 0.4552
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4626 - val_loss: 0.4578
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4616 - val_loss: 0.4619
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4622 - val_loss: 0.4597
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4607 - val_loss: 0.4561
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4614 - val_loss: 0.4566
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4617 - val_loss: 0.4595
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4626 - val_loss: 0.4540
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 17.15 seconds.
Calculating affinities...
Calculated affinities in 0.31 seconds.
Calculated graph and diffusion operator in 17.48 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.99 seconds.
Calculating KMeans...
Calculated KMeans in 15.27 seconds.
Calculated landmark operator in 17.77 seconds.
Calculating optimal t...
Automatically selected t = 25
Calculated optimal t in 3.10 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.59 seconds.
Calculating metric MDS...
Calculated metric MDS in 17.21 seconds.
Calculated PHATE in 56.17 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_377 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_378 (Dense) (None, 32) 2080 dense_377[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_378[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_378[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_379 (Dense) (None, 32) 128
_________________________________________________________________
dense_380 (Dense) (None, 64) 2112
_________________________________________________________________
dense_381 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_377 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_378 (Dense) (None, 32) 2080 dense_377[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_378[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_378[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1250 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1250 [()] 0 tf_op_layer_Shape_1250[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_125 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1250[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_125[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_375 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_125 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_375[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1251 (TensorF [(2,)] 0 tf_op_layer_Add_125[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1253 (TensorF [(2,)] 0 tf_op_layer_Add_125[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1252 (TensorF [(2,)] 0 tf_op_layer_Add_125[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1254 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1256 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1255 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1257 (TensorF [(2,)] 0 tf_op_layer_Add_125[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1259 (TensorF [(2,)] 0 tf_op_layer_Add_125[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1258 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1251 [()] 0 tf_op_layer_Shape_1251[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1253 [()] 0 tf_op_layer_Shape_1253[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1252 [()] 0 tf_op_layer_Shape_1252[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1254 [()] 0 tf_op_layer_Shape_1254[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1256 [()] 0 tf_op_layer_Shape_1256[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1255 [()] 0 tf_op_layer_Shape_1255[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1257 [()] 0 tf_op_layer_Shape_1257[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1259 [()] 0 tf_op_layer_Shape_1259[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1258 [()] 0 tf_op_layer_Shape_1258[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_750/shape ( [(3,)] 0 tf_op_layer_strided_slice_1251[0]
tf_op_layer_strided_slice_1253[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_751/shape ( [(3,)] 0 tf_op_layer_strided_slice_1252[0]
tf_op_layer_strided_slice_1253[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_752/shape ( [(3,)] 0 tf_op_layer_strided_slice_1254[0]
tf_op_layer_strided_slice_1256[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_753/shape ( [(3,)] 0 tf_op_layer_strided_slice_1255[0]
tf_op_layer_strided_slice_1256[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_754/shape ( [(3,)] 0 tf_op_layer_strided_slice_1257[0]
tf_op_layer_strided_slice_1259[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_755/shape ( [(3,)] 0 tf_op_layer_strided_slice_1258[0]
tf_op_layer_strided_slice_1259[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_750 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_125[0][0]
tf_op_layer_Reshape_750/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_750/multiples [(3,)] 0 tf_op_layer_strided_slice_1252[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_751 (Tensor [(1, None, None)] 0 tf_op_layer_Add_125[0][0]
tf_op_layer_Reshape_751/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_751/multiples [(3,)] 0 tf_op_layer_strided_slice_1251[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_752 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_752/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_752/multiples [(3,)] 0 tf_op_layer_strided_slice_1255[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_753 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_753/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_753/multiples [(3,)] 0 tf_op_layer_strided_slice_1254[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_754 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_125[0][0]
tf_op_layer_Reshape_754/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_754/multiples [(3,)] 0 tf_op_layer_strided_slice_1258[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_755 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_755/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_755/multiples [(3,)] 0 tf_op_layer_strided_slice_1257[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_750 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_750[0][0]
tf_op_layer_Tile_750/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_751 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_751[0][0]
tf_op_layer_Tile_751/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_752 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_752[0][0]
tf_op_layer_Tile_752/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_753 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_753[0][0]
tf_op_layer_Tile_753/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_754 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_754[0][0]
tf_op_layer_Tile_754/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_755 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_755[0][0]
tf_op_layer_Tile_755/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_625 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_750[0][0]
tf_op_layer_Tile_751[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_626 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_752[0][0]
tf_op_layer_Tile_753[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_627 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_754[0][0]
tf_op_layer_Tile_755[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_500 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_625[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_501 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_626[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_502 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_627[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_875 (TensorFlo [(None, None)] 0 tf_op_layer_Square_500[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_876 (TensorFlo [(None, None)] 0 tf_op_layer_Square_501[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_877 (TensorFlo [(None, None)] 0 tf_op_layer_Square_502[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_375 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_875[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_375 (TensorFlo [()] 0 tf_op_layer_strided_slice_1253[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_376 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_876[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_376 (TensorFlo [()] 0 tf_op_layer_strided_slice_1256[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_377 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_877[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_377 (TensorFlo [()] 0 tf_op_layer_strided_slice_1259[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_375 (Tensor [(None, None)] 0 tf_op_layer_Neg_375[0][0]
tf_op_layer_Cast_375[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_376 (Tensor [(None, None)] 0 tf_op_layer_Neg_376[0][0]
tf_op_layer_Cast_376[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_377 (Tensor [(None, None)] 0 tf_op_layer_Neg_377[0][0]
tf_op_layer_Cast_377[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_375 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_375[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_376 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_376[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_377 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_377[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_878 (TensorFlo [()] 0 tf_op_layer_Exp_375[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_879 (TensorFlo [()] 0 tf_op_layer_Exp_376[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_880 (TensorFlo [()] 0 tf_op_layer_Exp_377[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_629 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_250 (TensorFl [()] 0 tf_op_layer_Mean_878[0][0]
tf_op_layer_Mean_879[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_376 (TensorFlow [()] 0 tf_op_layer_Mean_880[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_503 (TensorF [(None, 97)] 0 tf_op_layer_Sub_629[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_628 (TensorFlow [()] 0 tf_op_layer_AddV2_250[0][0]
tf_op_layer_Mul_376[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_125 (TensorFlow [(None,)] 0 tf_op_layer_Square_503[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_377 (TensorFlow [()] 0 tf_op_layer_Sub_628[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_251 (TensorFl [(None,)] 0 tf_op_layer_Sum_125[0][0]
tf_op_layer_Mul_377[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_881 (TensorFlo [()] 0 tf_op_layer_AddV2_251[0][0]
__________________________________________________________________________________________________
add_loss_125 (AddLoss) () 0 tf_op_layer_Mean_881[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.9157WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0013s vs `on_train_batch_end` time: 0.0962s). Check your callbacks.
218/218 [==============================] - 1s 3ms/step - loss: 0.8796 - val_loss: 0.4517
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4162 - val_loss: 0.3935
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3863 - val_loss: 0.3771
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3770 - val_loss: 0.3665
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3741 - val_loss: 0.3693
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3687 - val_loss: 0.3630
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3654 - val_loss: 0.3611
Epoch 8/100
218/218 [==============================] - 0s 980us/step - loss: 0.3635 - val_loss: 0.3605
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3603 - val_loss: 0.3557
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3605 - val_loss: 0.3531
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3589 - val_loss: 0.3579
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3565 - val_loss: 0.3508
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3550 - val_loss: 0.3523
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3548 - val_loss: 0.3513
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3537 - val_loss: 0.3521
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3525 - val_loss: 0.3518
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3521 - val_loss: 0.3517
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3516 - val_loss: 0.3497
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3507 - val_loss: 0.3487
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3510 - val_loss: 0.3470
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3501 - val_loss: 0.3458
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3481
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3467
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3497 - val_loss: 0.3467
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3479
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3478
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3465
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3435
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3472
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3443
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3434
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3462
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3465
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3451
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3429
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3444
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3436
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3449
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3438
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3428
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3442
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3430
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3461
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3411
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3441
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3397
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3420
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3407
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3413
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3422
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3429
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3406
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3411
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3412
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3428
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3392
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3383
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3412
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3373
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3388
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3400
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3385
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3388
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3388 - val_loss: 0.3383
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3376
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3401
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3395 - val_loss: 0.3365
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3364
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3382 - val_loss: 0.3386
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3389 - val_loss: 0.3383
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3378
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3385
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3354
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3393 - val_loss: 0.3378
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3392 - val_loss: 0.3370
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3373
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3374 - val_loss: 0.3368
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3381 - val_loss: 0.3357
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3386 - val_loss: 0.3352
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3395 - val_loss: 0.3359
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3401 - val_loss: 0.3379
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3380 - val_loss: 0.3364
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3386 - val_loss: 0.3393
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3373 - val_loss: 0.3352
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3380 - val_loss: 0.3358
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3376 - val_loss: 0.3360
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3380 - val_loss: 0.3373
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3377 - val_loss: 0.3334
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3370 - val_loss: 0.3347
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3371 - val_loss: 0.3361
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3360 - val_loss: 0.3365
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3379 - val_loss: 0.3336
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3369 - val_loss: 0.3347
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3370 - val_loss: 0.3339
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3361 - val_loss: 0.3343
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3366 - val_loss: 0.3355
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3353 - val_loss: 0.3334
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3357 - val_loss: 0.3329
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3362 - val_loss: 0.3338
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3356 - val_loss: 0.3348
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_382 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1260 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1260 [()] 0 tf_op_layer_Shape_1260[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_126 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1260[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_126[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_378 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_126 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_378[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1261 (TensorF [(2,)] 0 tf_op_layer_Add_126[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1263 (TensorF [(2,)] 0 tf_op_layer_Add_126[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1262 (TensorF [(2,)] 0 tf_op_layer_Add_126[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1264 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1266 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1265 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1267 (TensorF [(2,)] 0 tf_op_layer_Add_126[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1269 (TensorF [(2,)] 0 tf_op_layer_Add_126[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1268 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1261 [()] 0 tf_op_layer_Shape_1261[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1263 [()] 0 tf_op_layer_Shape_1263[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1262 [()] 0 tf_op_layer_Shape_1262[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1264 [()] 0 tf_op_layer_Shape_1264[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1266 [()] 0 tf_op_layer_Shape_1266[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1265 [()] 0 tf_op_layer_Shape_1265[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1267 [()] 0 tf_op_layer_Shape_1267[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1269 [()] 0 tf_op_layer_Shape_1269[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1268 [()] 0 tf_op_layer_Shape_1268[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_756/shape ( [(3,)] 0 tf_op_layer_strided_slice_1261[0]
tf_op_layer_strided_slice_1263[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_757/shape ( [(3,)] 0 tf_op_layer_strided_slice_1262[0]
tf_op_layer_strided_slice_1263[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_758/shape ( [(3,)] 0 tf_op_layer_strided_slice_1264[0]
tf_op_layer_strided_slice_1266[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_759/shape ( [(3,)] 0 tf_op_layer_strided_slice_1265[0]
tf_op_layer_strided_slice_1266[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_760/shape ( [(3,)] 0 tf_op_layer_strided_slice_1267[0]
tf_op_layer_strided_slice_1269[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_761/shape ( [(3,)] 0 tf_op_layer_strided_slice_1268[0]
tf_op_layer_strided_slice_1269[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_756 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_126[0][0]
tf_op_layer_Reshape_756/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_756/multiples [(3,)] 0 tf_op_layer_strided_slice_1262[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_757 (Tensor [(1, None, None)] 0 tf_op_layer_Add_126[0][0]
tf_op_layer_Reshape_757/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_757/multiples [(3,)] 0 tf_op_layer_strided_slice_1261[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_758 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_758/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_758/multiples [(3,)] 0 tf_op_layer_strided_slice_1265[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_759 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_759/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_759/multiples [(3,)] 0 tf_op_layer_strided_slice_1264[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_760 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_126[0][0]
tf_op_layer_Reshape_760/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_760/multiples [(3,)] 0 tf_op_layer_strided_slice_1268[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_761 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_761/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_761/multiples [(3,)] 0 tf_op_layer_strided_slice_1267[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_756 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_756[0][0]
tf_op_layer_Tile_756/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_757 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_757[0][0]
tf_op_layer_Tile_757/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_758 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_758[0][0]
tf_op_layer_Tile_758/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_759 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_759[0][0]
tf_op_layer_Tile_759/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_760 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_760[0][0]
tf_op_layer_Tile_760/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_761 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_761[0][0]
tf_op_layer_Tile_761/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_630 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_756[0][0]
tf_op_layer_Tile_757[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_631 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_758[0][0]
tf_op_layer_Tile_759[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_632 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_760[0][0]
tf_op_layer_Tile_761[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_504 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_630[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_505 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_631[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_506 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_632[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_882 (TensorFlo [(None, None)] 0 tf_op_layer_Square_504[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_883 (TensorFlo [(None, None)] 0 tf_op_layer_Square_505[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_884 (TensorFlo [(None, None)] 0 tf_op_layer_Square_506[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_378 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_882[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_378 (TensorFlo [()] 0 tf_op_layer_strided_slice_1263[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_379 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_883[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_379 (TensorFlo [()] 0 tf_op_layer_strided_slice_1266[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_380 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_884[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_380 (TensorFlo [()] 0 tf_op_layer_strided_slice_1269[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_378 (Tensor [(None, None)] 0 tf_op_layer_Neg_378[0][0]
tf_op_layer_Cast_378[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_379 (Tensor [(None, None)] 0 tf_op_layer_Neg_379[0][0]
tf_op_layer_Cast_379[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_380 (Tensor [(None, None)] 0 tf_op_layer_Neg_380[0][0]
tf_op_layer_Cast_380[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_378 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_378[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_379 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_379[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_380 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_380[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_885 (TensorFlo [()] 0 tf_op_layer_Exp_378[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_886 (TensorFlo [()] 0 tf_op_layer_Exp_379[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_887 (TensorFlo [()] 0 tf_op_layer_Exp_380[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_634 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_252 (TensorFl [()] 0 tf_op_layer_Mean_885[0][0]
tf_op_layer_Mean_886[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_379 (TensorFlow [()] 0 tf_op_layer_Mean_887[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_507 (TensorF [(None, 97)] 0 tf_op_layer_Sub_634[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_633 (TensorFlow [()] 0 tf_op_layer_AddV2_252[0][0]
tf_op_layer_Mul_379[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_126 (TensorFlow [(None,)] 0 tf_op_layer_Square_507[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_380 (TensorFlow [()] 0 tf_op_layer_Sub_633[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_253 (TensorFl [(None,)] 0 tf_op_layer_Sum_126[0][0]
tf_op_layer_Mul_380[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_888 (TensorFlo [()] 0 tf_op_layer_AddV2_253[0][0]
__________________________________________________________________________________________________
add_loss_126 (AddLoss) () 0 tf_op_layer_Mean_888[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 14s - loss: 8.1361WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.1308s). Check your callbacks.
218/218 [==============================] - 1s 3ms/step - loss: 2.1479 - val_loss: 0.9617
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.8615 - val_loss: 0.7339
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.7159 - val_loss: 0.6392
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6414 - val_loss: 0.5897
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6028 - val_loss: 0.5633
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5789 - val_loss: 0.5496
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5663 - val_loss: 0.5364
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5556 - val_loss: 0.5304
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5445 - val_loss: 0.5194
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5363 - val_loss: 0.5052
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5305 - val_loss: 0.5017
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5232 - val_loss: 0.4959
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5210 - val_loss: 0.4949
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5141 - val_loss: 0.4896
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5128 - val_loss: 0.4865
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5119 - val_loss: 0.4803
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5083 - val_loss: 0.4794
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5035 - val_loss: 0.4773
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5022 - val_loss: 0.4816
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4997 - val_loss: 0.4790
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4965 - val_loss: 0.4713
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4950 - val_loss: 0.4682
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4909 - val_loss: 0.4710
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4955 - val_loss: 0.4646
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4906 - val_loss: 0.4669
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4903 - val_loss: 0.4652
Epoch 27/100
218/218 [==============================] - 0s 970us/step - loss: 0.4906 - val_loss: 0.4612
Epoch 28/100
218/218 [==============================] - 0s 980us/step - loss: 0.4888 - val_loss: 0.4651
Epoch 29/100
218/218 [==============================] - 0s 972us/step - loss: 0.4876 - val_loss: 0.4658
Epoch 30/100
218/218 [==============================] - 0s 973us/step - loss: 0.4864 - val_loss: 0.4586
Epoch 31/100
218/218 [==============================] - 0s 972us/step - loss: 0.4851 - val_loss: 0.4609
Epoch 32/100
218/218 [==============================] - 0s 980us/step - loss: 0.4850 - val_loss: 0.4598
Epoch 33/100
218/218 [==============================] - 0s 986us/step - loss: 0.4836 - val_loss: 0.4624
Epoch 34/100
218/218 [==============================] - 0s 979us/step - loss: 0.4820 - val_loss: 0.4570
Epoch 35/100
218/218 [==============================] - 0s 978us/step - loss: 0.4811 - val_loss: 0.4556
Epoch 36/100
218/218 [==============================] - 0s 979us/step - loss: 0.4820 - val_loss: 0.4582
Epoch 37/100
218/218 [==============================] - 0s 976us/step - loss: 0.4817 - val_loss: 0.4561
Epoch 38/100
218/218 [==============================] - 0s 975us/step - loss: 0.4786 - val_loss: 0.4603
Epoch 39/100
218/218 [==============================] - 0s 972us/step - loss: 0.4781 - val_loss: 0.4554
Epoch 40/100
218/218 [==============================] - 0s 968us/step - loss: 0.4770 - val_loss: 0.4567
Epoch 41/100
218/218 [==============================] - 0s 973us/step - loss: 0.4773 - val_loss: 0.4512
Epoch 42/100
218/218 [==============================] - 0s 985us/step - loss: 0.4752 - val_loss: 0.4557
Epoch 43/100
218/218 [==============================] - 0s 989us/step - loss: 0.4763 - val_loss: 0.4541
Epoch 44/100
218/218 [==============================] - 0s 973us/step - loss: 0.4757 - val_loss: 0.4571
Epoch 45/100
218/218 [==============================] - 0s 969us/step - loss: 0.4757 - val_loss: 0.4522
Epoch 46/100
218/218 [==============================] - 0s 969us/step - loss: 0.4750 - val_loss: 0.4504
Epoch 47/100
218/218 [==============================] - 0s 974us/step - loss: 0.4757 - val_loss: 0.4527
Epoch 48/100
218/218 [==============================] - 0s 978us/step - loss: 0.4758 - val_loss: 0.4486
Epoch 49/100
218/218 [==============================] - 0s 978us/step - loss: 0.4731 - val_loss: 0.4528
Epoch 50/100
218/218 [==============================] - 0s 973us/step - loss: 0.4723 - val_loss: 0.4550
Epoch 51/100
218/218 [==============================] - 0s 980us/step - loss: 0.4727 - val_loss: 0.4546
Epoch 52/100
218/218 [==============================] - 0s 975us/step - loss: 0.4726 - val_loss: 0.4510
Epoch 53/100
218/218 [==============================] - 0s 968us/step - loss: 0.4736 - val_loss: 0.4529
Epoch 54/100
218/218 [==============================] - 0s 983us/step - loss: 0.4727 - val_loss: 0.4482
Epoch 55/100
218/218 [==============================] - 0s 973us/step - loss: 0.4734 - val_loss: 0.4504
Epoch 56/100
218/218 [==============================] - 0s 981us/step - loss: 0.4725 - val_loss: 0.4516
Epoch 57/100
218/218 [==============================] - 0s 965us/step - loss: 0.4717 - val_loss: 0.4505
Epoch 58/100
218/218 [==============================] - 0s 975us/step - loss: 0.4704 - val_loss: 0.4484
Epoch 59/100
218/218 [==============================] - 0s 978us/step - loss: 0.4700 - val_loss: 0.4475
Epoch 60/100
218/218 [==============================] - 0s 976us/step - loss: 0.4716 - val_loss: 0.4490
Epoch 61/100
218/218 [==============================] - 0s 970us/step - loss: 0.4719 - val_loss: 0.4429
Epoch 62/100
218/218 [==============================] - 0s 972us/step - loss: 0.4694 - val_loss: 0.4466
Epoch 63/100
218/218 [==============================] - 0s 987us/step - loss: 0.4708 - val_loss: 0.4446
Epoch 64/100
218/218 [==============================] - 0s 986us/step - loss: 0.4693 - val_loss: 0.4467
Epoch 65/100
218/218 [==============================] - 0s 972us/step - loss: 0.4691 - val_loss: 0.4459
Epoch 66/100
218/218 [==============================] - 0s 979us/step - loss: 0.4696 - val_loss: 0.4438
Epoch 67/100
218/218 [==============================] - 0s 972us/step - loss: 0.4695 - val_loss: 0.4458
Epoch 68/100
218/218 [==============================] - 0s 940us/step - loss: 0.4666 - val_loss: 0.4435
Epoch 69/100
218/218 [==============================] - 0s 971us/step - loss: 0.4667 - val_loss: 0.4485
Epoch 70/100
218/218 [==============================] - 0s 993us/step - loss: 0.4667 - val_loss: 0.4434
Epoch 71/100
218/218 [==============================] - 0s 978us/step - loss: 0.4670 - val_loss: 0.4460
Epoch 72/100
218/218 [==============================] - 0s 965us/step - loss: 0.4679 - val_loss: 0.4423
Epoch 73/100
218/218 [==============================] - 0s 979us/step - loss: 0.4673 - val_loss: 0.4468
Epoch 74/100
218/218 [==============================] - 0s 974us/step - loss: 0.4674 - val_loss: 0.4432
Epoch 75/100
218/218 [==============================] - 0s 966us/step - loss: 0.4663 - val_loss: 0.4460
Epoch 76/100
218/218 [==============================] - 0s 968us/step - loss: 0.4676 - val_loss: 0.4438
Epoch 77/100
218/218 [==============================] - 0s 974us/step - loss: 0.4655 - val_loss: 0.4417
Epoch 78/100
218/218 [==============================] - 0s 975us/step - loss: 0.4647 - val_loss: 0.4455
Epoch 79/100
218/218 [==============================] - 0s 987us/step - loss: 0.4663 - val_loss: 0.4375
Epoch 80/100
218/218 [==============================] - 0s 967us/step - loss: 0.4653 - val_loss: 0.4393
Epoch 81/100
218/218 [==============================] - 0s 972us/step - loss: 0.4651 - val_loss: 0.4471
Epoch 82/100
218/218 [==============================] - 0s 978us/step - loss: 0.4662 - val_loss: 0.4413
Epoch 83/100
218/218 [==============================] - 0s 974us/step - loss: 0.4651 - val_loss: 0.4405
Epoch 84/100
218/218 [==============================] - 0s 979us/step - loss: 0.4654 - val_loss: 0.4408
Epoch 85/100
218/218 [==============================] - 0s 968us/step - loss: 0.4667 - val_loss: 0.4422
Epoch 86/100
218/218 [==============================] - 0s 972us/step - loss: 0.4642 - val_loss: 0.4384
Epoch 87/100
218/218 [==============================] - 0s 980us/step - loss: 0.4634 - val_loss: 0.4430
Epoch 88/100
218/218 [==============================] - 0s 981us/step - loss: 0.4638 - val_loss: 0.4444
Epoch 89/100
218/218 [==============================] - 0s 972us/step - loss: 0.4645 - val_loss: 0.4454
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4648 - val_loss: 0.4441
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4638 - val_loss: 0.4432
Epoch 92/100
218/218 [==============================] - 0s 981us/step - loss: 0.4654 - val_loss: 0.4439
Epoch 93/100
218/218 [==============================] - 0s 965us/step - loss: 0.4638 - val_loss: 0.4437
Epoch 94/100
218/218 [==============================] - 0s 958us/step - loss: 0.4639 - val_loss: 0.4403
Epoch 95/100
218/218 [==============================] - 0s 987us/step - loss: 0.4644 - val_loss: 0.4416
Epoch 96/100
218/218 [==============================] - 0s 975us/step - loss: 0.4632 - val_loss: 0.4439
Epoch 97/100
218/218 [==============================] - 0s 975us/step - loss: 0.4633 - val_loss: 0.4378
Epoch 98/100
218/218 [==============================] - 0s 992us/step - loss: 0.4639 - val_loss: 0.4377
Epoch 99/100
218/218 [==============================] - 0s 981us/step - loss: 0.4628 - val_loss: 0.4404
Epoch 100/100
218/218 [==============================] - 0s 988us/step - loss: 0.4637 - val_loss: 0.4402
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.99 seconds.
Calculating affinities...
Calculated affinities in 0.26 seconds.
Calculated graph and diffusion operator in 14.26 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.82 seconds.
Calculating KMeans...
Calculated KMeans in 11.73 seconds.
Calculated landmark operator in 13.91 seconds.
Calculating optimal t...
Automatically selected t = 25
Calculated optimal t in 2.08 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.45 seconds.
Calculating metric MDS...
Calculated metric MDS in 15.43 seconds.
Calculated PHATE in 46.15 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_383 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_384 (Dense) (None, 32) 2080 dense_383[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_384[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_384[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_385 (Dense) (None, 32) 128
_________________________________________________________________
dense_386 (Dense) (None, 64) 2112
_________________________________________________________________
dense_387 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_383 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_384 (Dense) (None, 32) 2080 dense_383[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_384[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_384[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1270 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1270 [()] 0 tf_op_layer_Shape_1270[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_127 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1270[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_127[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_381 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_127 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_381[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1271 (TensorF [(2,)] 0 tf_op_layer_Add_127[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1273 (TensorF [(2,)] 0 tf_op_layer_Add_127[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1272 (TensorF [(2,)] 0 tf_op_layer_Add_127[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1274 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1276 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1275 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1277 (TensorF [(2,)] 0 tf_op_layer_Add_127[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1279 (TensorF [(2,)] 0 tf_op_layer_Add_127[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1278 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1271 [()] 0 tf_op_layer_Shape_1271[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1273 [()] 0 tf_op_layer_Shape_1273[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1272 [()] 0 tf_op_layer_Shape_1272[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1274 [()] 0 tf_op_layer_Shape_1274[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1276 [()] 0 tf_op_layer_Shape_1276[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1275 [()] 0 tf_op_layer_Shape_1275[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1277 [()] 0 tf_op_layer_Shape_1277[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1279 [()] 0 tf_op_layer_Shape_1279[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1278 [()] 0 tf_op_layer_Shape_1278[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_762/shape ( [(3,)] 0 tf_op_layer_strided_slice_1271[0]
tf_op_layer_strided_slice_1273[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_763/shape ( [(3,)] 0 tf_op_layer_strided_slice_1272[0]
tf_op_layer_strided_slice_1273[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_764/shape ( [(3,)] 0 tf_op_layer_strided_slice_1274[0]
tf_op_layer_strided_slice_1276[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_765/shape ( [(3,)] 0 tf_op_layer_strided_slice_1275[0]
tf_op_layer_strided_slice_1276[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_766/shape ( [(3,)] 0 tf_op_layer_strided_slice_1277[0]
tf_op_layer_strided_slice_1279[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_767/shape ( [(3,)] 0 tf_op_layer_strided_slice_1278[0]
tf_op_layer_strided_slice_1279[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_762 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_127[0][0]
tf_op_layer_Reshape_762/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_762/multiples [(3,)] 0 tf_op_layer_strided_slice_1272[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_763 (Tensor [(1, None, None)] 0 tf_op_layer_Add_127[0][0]
tf_op_layer_Reshape_763/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_763/multiples [(3,)] 0 tf_op_layer_strided_slice_1271[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_764 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_764/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_764/multiples [(3,)] 0 tf_op_layer_strided_slice_1275[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_765 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_765/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_765/multiples [(3,)] 0 tf_op_layer_strided_slice_1274[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_766 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_127[0][0]
tf_op_layer_Reshape_766/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_766/multiples [(3,)] 0 tf_op_layer_strided_slice_1278[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_767 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_767/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_767/multiples [(3,)] 0 tf_op_layer_strided_slice_1277[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_762 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_762[0][0]
tf_op_layer_Tile_762/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_763 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_763[0][0]
tf_op_layer_Tile_763/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_764 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_764[0][0]
tf_op_layer_Tile_764/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_765 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_765[0][0]
tf_op_layer_Tile_765/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_766 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_766[0][0]
tf_op_layer_Tile_766/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_767 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_767[0][0]
tf_op_layer_Tile_767/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_635 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_762[0][0]
tf_op_layer_Tile_763[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_636 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_764[0][0]
tf_op_layer_Tile_765[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_637 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_766[0][0]
tf_op_layer_Tile_767[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_508 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_635[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_509 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_636[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_510 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_637[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_889 (TensorFlo [(None, None)] 0 tf_op_layer_Square_508[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_890 (TensorFlo [(None, None)] 0 tf_op_layer_Square_509[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_891 (TensorFlo [(None, None)] 0 tf_op_layer_Square_510[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_381 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_889[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_381 (TensorFlo [()] 0 tf_op_layer_strided_slice_1273[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_382 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_890[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_382 (TensorFlo [()] 0 tf_op_layer_strided_slice_1276[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_383 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_891[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_383 (TensorFlo [()] 0 tf_op_layer_strided_slice_1279[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_381 (Tensor [(None, None)] 0 tf_op_layer_Neg_381[0][0]
tf_op_layer_Cast_381[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_382 (Tensor [(None, None)] 0 tf_op_layer_Neg_382[0][0]
tf_op_layer_Cast_382[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_383 (Tensor [(None, None)] 0 tf_op_layer_Neg_383[0][0]
tf_op_layer_Cast_383[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_381 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_381[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_382 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_382[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_383 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_383[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_892 (TensorFlo [()] 0 tf_op_layer_Exp_381[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_893 (TensorFlo [()] 0 tf_op_layer_Exp_382[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_894 (TensorFlo [()] 0 tf_op_layer_Exp_383[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_639 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_254 (TensorFl [()] 0 tf_op_layer_Mean_892[0][0]
tf_op_layer_Mean_893[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_382 (TensorFlow [()] 0 tf_op_layer_Mean_894[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_511 (TensorF [(None, 97)] 0 tf_op_layer_Sub_639[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_638 (TensorFlow [()] 0 tf_op_layer_AddV2_254[0][0]
tf_op_layer_Mul_382[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_127 (TensorFlow [(None,)] 0 tf_op_layer_Square_511[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_383 (TensorFlow [()] 0 tf_op_layer_Sub_638[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_255 (TensorFl [(None,)] 0 tf_op_layer_Sum_127[0][0]
tf_op_layer_Mul_383[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_895 (TensorFlo [()] 0 tf_op_layer_AddV2_255[0][0]
__________________________________________________________________________________________________
add_loss_127 (AddLoss) () 0 tf_op_layer_Mean_895[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 13s - loss: 8.0092WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1227s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9169 - val_loss: 0.4905
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4238 - val_loss: 0.3995
Epoch 3/100
218/218 [==============================] - 0s 1000us/step - loss: 0.3852 - val_loss: 0.3869
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3774 - val_loss: 0.3837
Epoch 5/100
218/218 [==============================] - 0s 993us/step - loss: 0.3724 - val_loss: 0.3775
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3685 - val_loss: 0.3776
Epoch 7/100
218/218 [==============================] - 0s 997us/step - loss: 0.3655 - val_loss: 0.3736
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3632 - val_loss: 0.3687
Epoch 9/100
218/218 [==============================] - 0s 997us/step - loss: 0.3617 - val_loss: 0.3655
Epoch 10/100
218/218 [==============================] - 0s 995us/step - loss: 0.3599 - val_loss: 0.3679
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3590 - val_loss: 0.3668
Epoch 12/100
218/218 [==============================] - 0s 999us/step - loss: 0.3571 - val_loss: 0.3621
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3568 - val_loss: 0.3652
Epoch 14/100
218/218 [==============================] - 0s 998us/step - loss: 0.3552 - val_loss: 0.3643
Epoch 15/100
218/218 [==============================] - 0s 999us/step - loss: 0.3530 - val_loss: 0.3609
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3544 - val_loss: 0.3570
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3529 - val_loss: 0.3574
Epoch 18/100
218/218 [==============================] - 0s 999us/step - loss: 0.3522 - val_loss: 0.3586
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3520 - val_loss: 0.3575
Epoch 20/100
218/218 [==============================] - 0s 997us/step - loss: 0.3501 - val_loss: 0.3615
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3502 - val_loss: 0.3567
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3563
Epoch 23/100
218/218 [==============================] - 0s 999us/step - loss: 0.3488 - val_loss: 0.3591
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3497 - val_loss: 0.3566
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3594
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3568
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3560
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3551
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3474 - val_loss: 0.3529
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3581
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3549
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3542
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3529
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3528
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3526
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3531
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3538
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3539
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3536
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3514
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3538
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3529
Epoch 43/100
218/218 [==============================] - 0s 994us/step - loss: 0.3448 - val_loss: 0.3514
Epoch 44/100
218/218 [==============================] - 0s 967us/step - loss: 0.3452 - val_loss: 0.3526
Epoch 45/100
218/218 [==============================] - 0s 996us/step - loss: 0.3444 - val_loss: 0.3535
Epoch 46/100
218/218 [==============================] - 0s 965us/step - loss: 0.3448 - val_loss: 0.3531
Epoch 47/100
218/218 [==============================] - 0s 969us/step - loss: 0.3447 - val_loss: 0.3509
Epoch 48/100
218/218 [==============================] - 0s 985us/step - loss: 0.3435 - val_loss: 0.3510
Epoch 49/100
218/218 [==============================] - 0s 973us/step - loss: 0.3449 - val_loss: 0.3514
Epoch 50/100
218/218 [==============================] - 0s 969us/step - loss: 0.3455 - val_loss: 0.3512
Epoch 51/100
218/218 [==============================] - 0s 968us/step - loss: 0.3452 - val_loss: 0.3534
Epoch 52/100
218/218 [==============================] - 0s 970us/step - loss: 0.3435 - val_loss: 0.3516
Epoch 53/100
218/218 [==============================] - 0s 970us/step - loss: 0.3443 - val_loss: 0.3539
Epoch 54/100
218/218 [==============================] - 0s 967us/step - loss: 0.3436 - val_loss: 0.3523
Epoch 55/100
218/218 [==============================] - 0s 982us/step - loss: 0.3440 - val_loss: 0.3513
Epoch 56/100
218/218 [==============================] - 0s 996us/step - loss: 0.3437 - val_loss: 0.3496
Epoch 57/100
218/218 [==============================] - 0s 998us/step - loss: 0.3442 - val_loss: 0.3500
Epoch 58/100
218/218 [==============================] - 0s 993us/step - loss: 0.3440 - val_loss: 0.3498
Epoch 59/100
218/218 [==============================] - 0s 999us/step - loss: 0.3432 - val_loss: 0.3494
Epoch 60/100
218/218 [==============================] - 0s 996us/step - loss: 0.3434 - val_loss: 0.3515
Epoch 61/100
218/218 [==============================] - 0s 998us/step - loss: 0.3436 - val_loss: 0.3512
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3517
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3495
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3490
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3503
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3500
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3479
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3497
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3532
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3480
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3511
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3501
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3520
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3490
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3514
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3509
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3490
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3498
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3480
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3505
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3508
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3490
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3503
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3487
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3495
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3487
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3517
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3478
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3481
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3484
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3500
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3473
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3493
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3467
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3477
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3490
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3496
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3505
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3495
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3459
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_388 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1280 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1280 [()] 0 tf_op_layer_Shape_1280[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_128 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1280[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_128[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_384 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_128 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_384[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1281 (TensorF [(2,)] 0 tf_op_layer_Add_128[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1283 (TensorF [(2,)] 0 tf_op_layer_Add_128[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1282 (TensorF [(2,)] 0 tf_op_layer_Add_128[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1284 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1286 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1285 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1287 (TensorF [(2,)] 0 tf_op_layer_Add_128[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1289 (TensorF [(2,)] 0 tf_op_layer_Add_128[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1288 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1281 [()] 0 tf_op_layer_Shape_1281[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1283 [()] 0 tf_op_layer_Shape_1283[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1282 [()] 0 tf_op_layer_Shape_1282[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1284 [()] 0 tf_op_layer_Shape_1284[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1286 [()] 0 tf_op_layer_Shape_1286[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1285 [()] 0 tf_op_layer_Shape_1285[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1287 [()] 0 tf_op_layer_Shape_1287[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1289 [()] 0 tf_op_layer_Shape_1289[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1288 [()] 0 tf_op_layer_Shape_1288[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_768/shape ( [(3,)] 0 tf_op_layer_strided_slice_1281[0]
tf_op_layer_strided_slice_1283[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_769/shape ( [(3,)] 0 tf_op_layer_strided_slice_1282[0]
tf_op_layer_strided_slice_1283[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_770/shape ( [(3,)] 0 tf_op_layer_strided_slice_1284[0]
tf_op_layer_strided_slice_1286[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_771/shape ( [(3,)] 0 tf_op_layer_strided_slice_1285[0]
tf_op_layer_strided_slice_1286[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_772/shape ( [(3,)] 0 tf_op_layer_strided_slice_1287[0]
tf_op_layer_strided_slice_1289[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_773/shape ( [(3,)] 0 tf_op_layer_strided_slice_1288[0]
tf_op_layer_strided_slice_1289[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_768 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_128[0][0]
tf_op_layer_Reshape_768/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_768/multiples [(3,)] 0 tf_op_layer_strided_slice_1282[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_769 (Tensor [(1, None, None)] 0 tf_op_layer_Add_128[0][0]
tf_op_layer_Reshape_769/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_769/multiples [(3,)] 0 tf_op_layer_strided_slice_1281[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_770 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_770/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_770/multiples [(3,)] 0 tf_op_layer_strided_slice_1285[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_771 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_771/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_771/multiples [(3,)] 0 tf_op_layer_strided_slice_1284[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_772 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_128[0][0]
tf_op_layer_Reshape_772/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_772/multiples [(3,)] 0 tf_op_layer_strided_slice_1288[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_773 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_773/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_773/multiples [(3,)] 0 tf_op_layer_strided_slice_1287[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_768 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_768[0][0]
tf_op_layer_Tile_768/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_769 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_769[0][0]
tf_op_layer_Tile_769/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_770 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_770[0][0]
tf_op_layer_Tile_770/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_771 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_771[0][0]
tf_op_layer_Tile_771/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_772 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_772[0][0]
tf_op_layer_Tile_772/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_773 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_773[0][0]
tf_op_layer_Tile_773/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_640 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_768[0][0]
tf_op_layer_Tile_769[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_641 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_770[0][0]
tf_op_layer_Tile_771[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_642 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_772[0][0]
tf_op_layer_Tile_773[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_512 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_640[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_513 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_641[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_514 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_642[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_896 (TensorFlo [(None, None)] 0 tf_op_layer_Square_512[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_897 (TensorFlo [(None, None)] 0 tf_op_layer_Square_513[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_898 (TensorFlo [(None, None)] 0 tf_op_layer_Square_514[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_384 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_896[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_384 (TensorFlo [()] 0 tf_op_layer_strided_slice_1283[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_385 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_897[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_385 (TensorFlo [()] 0 tf_op_layer_strided_slice_1286[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_386 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_898[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_386 (TensorFlo [()] 0 tf_op_layer_strided_slice_1289[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_384 (Tensor [(None, None)] 0 tf_op_layer_Neg_384[0][0]
tf_op_layer_Cast_384[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_385 (Tensor [(None, None)] 0 tf_op_layer_Neg_385[0][0]
tf_op_layer_Cast_385[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_386 (Tensor [(None, None)] 0 tf_op_layer_Neg_386[0][0]
tf_op_layer_Cast_386[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_384 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_384[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_385 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_385[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_386 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_386[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_899 (TensorFlo [()] 0 tf_op_layer_Exp_384[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_900 (TensorFlo [()] 0 tf_op_layer_Exp_385[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_901 (TensorFlo [()] 0 tf_op_layer_Exp_386[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_644 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_256 (TensorFl [()] 0 tf_op_layer_Mean_899[0][0]
tf_op_layer_Mean_900[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_385 (TensorFlow [()] 0 tf_op_layer_Mean_901[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_515 (TensorF [(None, 97)] 0 tf_op_layer_Sub_644[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_643 (TensorFlow [()] 0 tf_op_layer_AddV2_256[0][0]
tf_op_layer_Mul_385[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_128 (TensorFlow [(None,)] 0 tf_op_layer_Square_515[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_386 (TensorFlow [()] 0 tf_op_layer_Sub_643[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_257 (TensorFl [(None,)] 0 tf_op_layer_Sum_128[0][0]
tf_op_layer_Mul_386[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_902 (TensorFlo [()] 0 tf_op_layer_AddV2_257[0][0]
__________________________________________________________________________________________________
add_loss_128 (AddLoss) () 0 tf_op_layer_Mean_902[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 7.9391WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0010s vs `on_train_batch_end` time: 0.1088s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 2.0905 - val_loss: 1.0155
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.8420 - val_loss: 0.7521
Epoch 3/100
218/218 [==============================] - 0s 991us/step - loss: 0.6928 - val_loss: 0.6715
Epoch 4/100
218/218 [==============================] - 0s 932us/step - loss: 0.6371 - val_loss: 0.6203
Epoch 5/100
218/218 [==============================] - 0s 946us/step - loss: 0.6053 - val_loss: 0.5985
Epoch 6/100
218/218 [==============================] - 0s 903us/step - loss: 0.5840 - val_loss: 0.5835
Epoch 7/100
218/218 [==============================] - 0s 863us/step - loss: 0.5682 - val_loss: 0.5668
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5575 - val_loss: 0.5603
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5449 - val_loss: 0.5441
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5370 - val_loss: 0.5376
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5325 - val_loss: 0.5302
Epoch 12/100
218/218 [==============================] - 0s 953us/step - loss: 0.5240 - val_loss: 0.5323
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5219 - val_loss: 0.5218
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5155 - val_loss: 0.5167
Epoch 15/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5116 - val_loss: 0.5199
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5115 - val_loss: 0.5136
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5055 - val_loss: 0.5079
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5023 - val_loss: 0.5054
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5003 - val_loss: 0.5053
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4973 - val_loss: 0.5012
Epoch 21/100
218/218 [==============================] - 0s 969us/step - loss: 0.4938 - val_loss: 0.4938
Epoch 22/100
218/218 [==============================] - 0s 981us/step - loss: 0.4956 - val_loss: 0.4961
Epoch 23/100
218/218 [==============================] - 0s 976us/step - loss: 0.4906 - val_loss: 0.4924
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4893 - val_loss: 0.4907
Epoch 25/100
218/218 [==============================] - 0s 985us/step - loss: 0.4884 - val_loss: 0.4895
Epoch 26/100
218/218 [==============================] - 0s 995us/step - loss: 0.4855 - val_loss: 0.4836
Epoch 27/100
218/218 [==============================] - 0s 989us/step - loss: 0.4874 - val_loss: 0.4857
Epoch 28/100
218/218 [==============================] - 0s 972us/step - loss: 0.4834 - val_loss: 0.4887
Epoch 29/100
218/218 [==============================] - 0s 986us/step - loss: 0.4831 - val_loss: 0.4818
Epoch 30/100
218/218 [==============================] - 0s 976us/step - loss: 0.4831 - val_loss: 0.4842
Epoch 31/100
218/218 [==============================] - 0s 992us/step - loss: 0.4797 - val_loss: 0.4878
Epoch 32/100
218/218 [==============================] - 0s 975us/step - loss: 0.4787 - val_loss: 0.4834
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4775 - val_loss: 0.4772
Epoch 34/100
218/218 [==============================] - 0s 980us/step - loss: 0.4767 - val_loss: 0.4833
Epoch 35/100
218/218 [==============================] - 0s 973us/step - loss: 0.4767 - val_loss: 0.4768
Epoch 36/100
218/218 [==============================] - 0s 982us/step - loss: 0.4740 - val_loss: 0.4764
Epoch 37/100
218/218 [==============================] - 0s 994us/step - loss: 0.4743 - val_loss: 0.4754
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4741 - val_loss: 0.4787
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4725 - val_loss: 0.4751
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4751 - val_loss: 0.4751
Epoch 41/100
218/218 [==============================] - 0s 986us/step - loss: 0.4725 - val_loss: 0.4725
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4717 - val_loss: 0.4728
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4714 - val_loss: 0.4759
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4705 - val_loss: 0.4714
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4695 - val_loss: 0.4749
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4697 - val_loss: 0.4717
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4680 - val_loss: 0.4729
Epoch 48/100
218/218 [==============================] - 0s 1000us/step - loss: 0.4695 - val_loss: 0.4742
Epoch 49/100
218/218 [==============================] - 0s 999us/step - loss: 0.4685 - val_loss: 0.4711
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4674 - val_loss: 0.4749
Epoch 51/100
218/218 [==============================] - 0s 996us/step - loss: 0.4651 - val_loss: 0.4678
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4674 - val_loss: 0.4683
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4664 - val_loss: 0.4709
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4674 - val_loss: 0.4661
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4670 - val_loss: 0.4708
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4655 - val_loss: 0.4671
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4633 - val_loss: 0.4727
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4633 - val_loss: 0.4700
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4646 - val_loss: 0.4707
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4645 - val_loss: 0.4691
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4635 - val_loss: 0.4620
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4620 - val_loss: 0.4678
Epoch 63/100
218/218 [==============================] - 0s 992us/step - loss: 0.4641 - val_loss: 0.4645
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4645 - val_loss: 0.4661
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4617 - val_loss: 0.4630
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4608 - val_loss: 0.4668
Epoch 67/100
218/218 [==============================] - 0s 998us/step - loss: 0.4614 - val_loss: 0.4652
Epoch 68/100
218/218 [==============================] - 0s 999us/step - loss: 0.4613 - val_loss: 0.4696
Epoch 69/100
218/218 [==============================] - 0s 997us/step - loss: 0.4618 - val_loss: 0.4650
Epoch 70/100
218/218 [==============================] - 0s 992us/step - loss: 0.4606 - val_loss: 0.4654
Epoch 71/100
218/218 [==============================] - 0s 994us/step - loss: 0.4626 - val_loss: 0.4613
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4610 - val_loss: 0.4633
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4645
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4608 - val_loss: 0.4656
Epoch 75/100
218/218 [==============================] - 0s 996us/step - loss: 0.4607 - val_loss: 0.4624
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4621
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4600 - val_loss: 0.4632
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4603 - val_loss: 0.4640
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4630
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4615 - val_loss: 0.4614
Epoch 81/100
218/218 [==============================] - 0s 996us/step - loss: 0.4616 - val_loss: 0.4627
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4592 - val_loss: 0.4611
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4599 - val_loss: 0.4636
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4573 - val_loss: 0.4588
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4591 - val_loss: 0.4585
Epoch 86/100
218/218 [==============================] - 0s 966us/step - loss: 0.4582 - val_loss: 0.4622
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4580 - val_loss: 0.4603
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4593 - val_loss: 0.4698
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4597 - val_loss: 0.4587
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4577 - val_loss: 0.4621
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4572 - val_loss: 0.4650
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4571 - val_loss: 0.4563
Epoch 93/100
218/218 [==============================] - 0s 987us/step - loss: 0.4570 - val_loss: 0.4618
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4583 - val_loss: 0.4613
Epoch 95/100
218/218 [==============================] - 0s 996us/step - loss: 0.4580 - val_loss: 0.4585
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4558 - val_loss: 0.4601
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4577 - val_loss: 0.4631
Epoch 98/100
218/218 [==============================] - 0s 996us/step - loss: 0.4571 - val_loss: 0.4595
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4575 - val_loss: 0.4609
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4562 - val_loss: 0.4604
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.86 seconds.
Calculating affinities...
Calculated affinities in 0.30 seconds.
Calculated graph and diffusion operator in 14.18 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.92 seconds.
Calculating KMeans...
Calculated KMeans in 14.88 seconds.
Calculated landmark operator in 17.26 seconds.
Calculating optimal t...
Automatically selected t = 33
Calculated optimal t in 2.45 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.52 seconds.
Calculating metric MDS...
Calculated metric MDS in 18.00 seconds.
Calculated PHATE in 52.42 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_389 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_390 (Dense) (None, 32) 2080 dense_389[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_390[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_390[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_391 (Dense) (None, 32) 128
_________________________________________________________________
dense_392 (Dense) (None, 64) 2112
_________________________________________________________________
dense_393 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_389 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_390 (Dense) (None, 32) 2080 dense_389[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_390[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_390[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1290 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1290 [()] 0 tf_op_layer_Shape_1290[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_129 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1290[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_129[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_387 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_129 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_387[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1291 (TensorF [(2,)] 0 tf_op_layer_Add_129[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1293 (TensorF [(2,)] 0 tf_op_layer_Add_129[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1292 (TensorF [(2,)] 0 tf_op_layer_Add_129[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1294 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1296 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1295 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1297 (TensorF [(2,)] 0 tf_op_layer_Add_129[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1299 (TensorF [(2,)] 0 tf_op_layer_Add_129[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1298 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1291 [()] 0 tf_op_layer_Shape_1291[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1293 [()] 0 tf_op_layer_Shape_1293[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1292 [()] 0 tf_op_layer_Shape_1292[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1294 [()] 0 tf_op_layer_Shape_1294[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1296 [()] 0 tf_op_layer_Shape_1296[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1295 [()] 0 tf_op_layer_Shape_1295[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1297 [()] 0 tf_op_layer_Shape_1297[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1299 [()] 0 tf_op_layer_Shape_1299[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1298 [()] 0 tf_op_layer_Shape_1298[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_774/shape ( [(3,)] 0 tf_op_layer_strided_slice_1291[0]
tf_op_layer_strided_slice_1293[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_775/shape ( [(3,)] 0 tf_op_layer_strided_slice_1292[0]
tf_op_layer_strided_slice_1293[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_776/shape ( [(3,)] 0 tf_op_layer_strided_slice_1294[0]
tf_op_layer_strided_slice_1296[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_777/shape ( [(3,)] 0 tf_op_layer_strided_slice_1295[0]
tf_op_layer_strided_slice_1296[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_778/shape ( [(3,)] 0 tf_op_layer_strided_slice_1297[0]
tf_op_layer_strided_slice_1299[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_779/shape ( [(3,)] 0 tf_op_layer_strided_slice_1298[0]
tf_op_layer_strided_slice_1299[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_774 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_129[0][0]
tf_op_layer_Reshape_774/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_774/multiples [(3,)] 0 tf_op_layer_strided_slice_1292[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_775 (Tensor [(1, None, None)] 0 tf_op_layer_Add_129[0][0]
tf_op_layer_Reshape_775/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_775/multiples [(3,)] 0 tf_op_layer_strided_slice_1291[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_776 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_776/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_776/multiples [(3,)] 0 tf_op_layer_strided_slice_1295[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_777 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_777/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_777/multiples [(3,)] 0 tf_op_layer_strided_slice_1294[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_778 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_129[0][0]
tf_op_layer_Reshape_778/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_778/multiples [(3,)] 0 tf_op_layer_strided_slice_1298[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_779 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_779/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_779/multiples [(3,)] 0 tf_op_layer_strided_slice_1297[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_774 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_774[0][0]
tf_op_layer_Tile_774/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_775 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_775[0][0]
tf_op_layer_Tile_775/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_776 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_776[0][0]
tf_op_layer_Tile_776/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_777 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_777[0][0]
tf_op_layer_Tile_777/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_778 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_778[0][0]
tf_op_layer_Tile_778/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_779 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_779[0][0]
tf_op_layer_Tile_779/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_645 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_774[0][0]
tf_op_layer_Tile_775[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_646 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_776[0][0]
tf_op_layer_Tile_777[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_647 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_778[0][0]
tf_op_layer_Tile_779[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_516 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_645[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_517 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_646[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_518 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_647[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_903 (TensorFlo [(None, None)] 0 tf_op_layer_Square_516[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_904 (TensorFlo [(None, None)] 0 tf_op_layer_Square_517[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_905 (TensorFlo [(None, None)] 0 tf_op_layer_Square_518[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_387 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_903[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_387 (TensorFlo [()] 0 tf_op_layer_strided_slice_1293[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_388 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_904[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_388 (TensorFlo [()] 0 tf_op_layer_strided_slice_1296[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_389 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_905[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_389 (TensorFlo [()] 0 tf_op_layer_strided_slice_1299[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_387 (Tensor [(None, None)] 0 tf_op_layer_Neg_387[0][0]
tf_op_layer_Cast_387[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_388 (Tensor [(None, None)] 0 tf_op_layer_Neg_388[0][0]
tf_op_layer_Cast_388[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_389 (Tensor [(None, None)] 0 tf_op_layer_Neg_389[0][0]
tf_op_layer_Cast_389[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_387 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_387[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_388 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_388[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_389 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_389[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_906 (TensorFlo [()] 0 tf_op_layer_Exp_387[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_907 (TensorFlo [()] 0 tf_op_layer_Exp_388[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_908 (TensorFlo [()] 0 tf_op_layer_Exp_389[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_649 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_258 (TensorFl [()] 0 tf_op_layer_Mean_906[0][0]
tf_op_layer_Mean_907[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_388 (TensorFlow [()] 0 tf_op_layer_Mean_908[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_519 (TensorF [(None, 97)] 0 tf_op_layer_Sub_649[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_648 (TensorFlow [()] 0 tf_op_layer_AddV2_258[0][0]
tf_op_layer_Mul_388[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_129 (TensorFlow [(None,)] 0 tf_op_layer_Square_519[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_389 (TensorFlow [()] 0 tf_op_layer_Sub_648[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_259 (TensorFl [(None,)] 0 tf_op_layer_Sum_129[0][0]
tf_op_layer_Mul_389[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_909 (TensorFlo [()] 0 tf_op_layer_AddV2_259[0][0]
__________________________________________________________________________________________________
add_loss_129 (AddLoss) () 0 tf_op_layer_Mean_909[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 7.9120WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1061s). Check your callbacks.
218/218 [==============================] - 1s 3ms/step - loss: 0.9266 - val_loss: 0.5195
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4810 - val_loss: 0.4429
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4083 - val_loss: 0.3914
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3824 - val_loss: 0.3887
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3768 - val_loss: 0.3757
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3722 - val_loss: 0.3766
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3686 - val_loss: 0.3694
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3669 - val_loss: 0.3677
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3651 - val_loss: 0.3635
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3612 - val_loss: 0.3636
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3604 - val_loss: 0.3612
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3601 - val_loss: 0.3593
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3563 - val_loss: 0.3597
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3574 - val_loss: 0.3611
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3558 - val_loss: 0.3577
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3570 - val_loss: 0.3564
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3535 - val_loss: 0.3532
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3532 - val_loss: 0.3591
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3542 - val_loss: 0.3531
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3519 - val_loss: 0.3566
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3529 - val_loss: 0.3533
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3516 - val_loss: 0.3540
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3524 - val_loss: 0.3534
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3499 - val_loss: 0.3520
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3495 - val_loss: 0.3529
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3513 - val_loss: 0.3520
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3519
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3519
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3500
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3484 - val_loss: 0.3511
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3506
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3519
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3472 - val_loss: 0.3536
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3506
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3466 - val_loss: 0.3505
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3483 - val_loss: 0.3496
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3519
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3525
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3503
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3499
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3507
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3512
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3490
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3491
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3518
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3516
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3490
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3464
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3512
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3524
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3481
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3472
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3478
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3509
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3486
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3440 - val_loss: 0.3495
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3500
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3463
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3494
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3507
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3482
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3490
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3479
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3487
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3476
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3477
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3467
Epoch 68/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3433 - val_loss: 0.3475
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3484
Epoch 70/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3429 - val_loss: 0.3487
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3497
Epoch 72/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3434 - val_loss: 0.3479
Epoch 73/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3424 - val_loss: 0.3462
Epoch 74/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3423 - val_loss: 0.3476
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3461
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3439
Epoch 77/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3425 - val_loss: 0.3455
Epoch 78/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3434 - val_loss: 0.3456
Epoch 79/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3435 - val_loss: 0.3468
Epoch 80/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3422 - val_loss: 0.3467
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3470
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3490
Epoch 83/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3416 - val_loss: 0.3475
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3453
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3457
Epoch 86/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3422 - val_loss: 0.3462
Epoch 87/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3419 - val_loss: 0.3455
Epoch 88/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3425 - val_loss: 0.3457
Epoch 89/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3419 - val_loss: 0.3479
Epoch 90/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3411 - val_loss: 0.3478
Epoch 91/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3402 - val_loss: 0.3447
Epoch 92/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3408 - val_loss: 0.3450
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3426 - val_loss: 0.3447
Epoch 94/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3411 - val_loss: 0.3468
Epoch 95/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3393 - val_loss: 0.3453
Epoch 96/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3411 - val_loss: 0.3458
Epoch 97/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3407 - val_loss: 0.3465
Epoch 98/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3420 - val_loss: 0.3478
Epoch 99/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3401 - val_loss: 0.3447
Epoch 100/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3413 - val_loss: 0.3473
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_394 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1300 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1300 [()] 0 tf_op_layer_Shape_1300[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_130 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1300[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_130[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_390 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_130 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_390[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1301 (TensorF [(2,)] 0 tf_op_layer_Add_130[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1303 (TensorF [(2,)] 0 tf_op_layer_Add_130[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1302 (TensorF [(2,)] 0 tf_op_layer_Add_130[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1304 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1306 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1305 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1307 (TensorF [(2,)] 0 tf_op_layer_Add_130[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1309 (TensorF [(2,)] 0 tf_op_layer_Add_130[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1308 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1301 [()] 0 tf_op_layer_Shape_1301[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1303 [()] 0 tf_op_layer_Shape_1303[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1302 [()] 0 tf_op_layer_Shape_1302[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1304 [()] 0 tf_op_layer_Shape_1304[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1306 [()] 0 tf_op_layer_Shape_1306[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1305 [()] 0 tf_op_layer_Shape_1305[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1307 [()] 0 tf_op_layer_Shape_1307[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1309 [()] 0 tf_op_layer_Shape_1309[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1308 [()] 0 tf_op_layer_Shape_1308[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_780/shape ( [(3,)] 0 tf_op_layer_strided_slice_1301[0]
tf_op_layer_strided_slice_1303[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_781/shape ( [(3,)] 0 tf_op_layer_strided_slice_1302[0]
tf_op_layer_strided_slice_1303[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_782/shape ( [(3,)] 0 tf_op_layer_strided_slice_1304[0]
tf_op_layer_strided_slice_1306[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_783/shape ( [(3,)] 0 tf_op_layer_strided_slice_1305[0]
tf_op_layer_strided_slice_1306[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_784/shape ( [(3,)] 0 tf_op_layer_strided_slice_1307[0]
tf_op_layer_strided_slice_1309[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_785/shape ( [(3,)] 0 tf_op_layer_strided_slice_1308[0]
tf_op_layer_strided_slice_1309[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_780 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_130[0][0]
tf_op_layer_Reshape_780/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_780/multiples [(3,)] 0 tf_op_layer_strided_slice_1302[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_781 (Tensor [(1, None, None)] 0 tf_op_layer_Add_130[0][0]
tf_op_layer_Reshape_781/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_781/multiples [(3,)] 0 tf_op_layer_strided_slice_1301[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_782 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_782/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_782/multiples [(3,)] 0 tf_op_layer_strided_slice_1305[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_783 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_783/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_783/multiples [(3,)] 0 tf_op_layer_strided_slice_1304[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_784 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_130[0][0]
tf_op_layer_Reshape_784/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_784/multiples [(3,)] 0 tf_op_layer_strided_slice_1308[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_785 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_785/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_785/multiples [(3,)] 0 tf_op_layer_strided_slice_1307[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_780 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_780[0][0]
tf_op_layer_Tile_780/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_781 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_781[0][0]
tf_op_layer_Tile_781/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_782 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_782[0][0]
tf_op_layer_Tile_782/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_783 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_783[0][0]
tf_op_layer_Tile_783/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_784 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_784[0][0]
tf_op_layer_Tile_784/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_785 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_785[0][0]
tf_op_layer_Tile_785/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_650 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_780[0][0]
tf_op_layer_Tile_781[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_651 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_782[0][0]
tf_op_layer_Tile_783[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_652 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_784[0][0]
tf_op_layer_Tile_785[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_520 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_650[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_521 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_651[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_522 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_652[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_910 (TensorFlo [(None, None)] 0 tf_op_layer_Square_520[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_911 (TensorFlo [(None, None)] 0 tf_op_layer_Square_521[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_912 (TensorFlo [(None, None)] 0 tf_op_layer_Square_522[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_390 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_910[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_390 (TensorFlo [()] 0 tf_op_layer_strided_slice_1303[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_391 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_911[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_391 (TensorFlo [()] 0 tf_op_layer_strided_slice_1306[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_392 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_912[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_392 (TensorFlo [()] 0 tf_op_layer_strided_slice_1309[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_390 (Tensor [(None, None)] 0 tf_op_layer_Neg_390[0][0]
tf_op_layer_Cast_390[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_391 (Tensor [(None, None)] 0 tf_op_layer_Neg_391[0][0]
tf_op_layer_Cast_391[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_392 (Tensor [(None, None)] 0 tf_op_layer_Neg_392[0][0]
tf_op_layer_Cast_392[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_390 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_390[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_391 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_391[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_392 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_392[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_913 (TensorFlo [()] 0 tf_op_layer_Exp_390[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_914 (TensorFlo [()] 0 tf_op_layer_Exp_391[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_915 (TensorFlo [()] 0 tf_op_layer_Exp_392[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_654 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_260 (TensorFl [()] 0 tf_op_layer_Mean_913[0][0]
tf_op_layer_Mean_914[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_391 (TensorFlow [()] 0 tf_op_layer_Mean_915[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_523 (TensorF [(None, 97)] 0 tf_op_layer_Sub_654[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_653 (TensorFlow [()] 0 tf_op_layer_AddV2_260[0][0]
tf_op_layer_Mul_391[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_130 (TensorFlow [(None,)] 0 tf_op_layer_Square_523[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_392 (TensorFlow [()] 0 tf_op_layer_Sub_653[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_261 (TensorFl [(None,)] 0 tf_op_layer_Sum_130[0][0]
tf_op_layer_Mul_392[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_916 (TensorFlo [()] 0 tf_op_layer_AddV2_261[0][0]
__________________________________________________________________________________________________
add_loss_130 (AddLoss) () 0 tf_op_layer_Mean_916[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 16s - loss: 8.1869WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.1553s). Check your callbacks.
218/218 [==============================] - 1s 4ms/step - loss: 2.0638 - val_loss: 0.9892
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.8602 - val_loss: 0.7448
Epoch 3/100
218/218 [==============================] - 0s 2ms/step - loss: 0.7147 - val_loss: 0.6627
Epoch 4/100
218/218 [==============================] - 0s 2ms/step - loss: 0.6449 - val_loss: 0.6112
Epoch 5/100
218/218 [==============================] - 0s 2ms/step - loss: 0.6126 - val_loss: 0.5866
Epoch 6/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5886 - val_loss: 0.5693
Epoch 7/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5728 - val_loss: 0.5560
Epoch 8/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5590 - val_loss: 0.5445
Epoch 9/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5495 - val_loss: 0.5308
Epoch 10/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5388 - val_loss: 0.5328
Epoch 11/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5319 - val_loss: 0.5216
Epoch 12/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5286 - val_loss: 0.5214
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5222 - val_loss: 0.5078
Epoch 14/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5201 - val_loss: 0.5117
Epoch 15/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5141 - val_loss: 0.5000
Epoch 16/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5143 - val_loss: 0.5033
Epoch 17/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5065 - val_loss: 0.5018
Epoch 18/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5041 - val_loss: 0.4985
Epoch 19/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4995 - val_loss: 0.4899
Epoch 20/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5011 - val_loss: 0.4913
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4981 - val_loss: 0.4922
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4950 - val_loss: 0.4803
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4938 - val_loss: 0.4889
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4918 - val_loss: 0.4865
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4900 - val_loss: 0.4810
Epoch 26/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4892 - val_loss: 0.4806
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4881 - val_loss: 0.4785
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4842 - val_loss: 0.4808
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4849 - val_loss: 0.4818
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4855 - val_loss: 0.4734
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4817 - val_loss: 0.4791
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4814 - val_loss: 0.4715
Epoch 33/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4826 - val_loss: 0.4717
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4812 - val_loss: 0.4731
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4803 - val_loss: 0.4757
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4781 - val_loss: 0.4730
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4790 - val_loss: 0.4675
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4797 - val_loss: 0.4692
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4738 - val_loss: 0.4721
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4757 - val_loss: 0.4716
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4753 - val_loss: 0.4702
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4733 - val_loss: 0.4649
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4751 - val_loss: 0.4660
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4733 - val_loss: 0.4648
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4711 - val_loss: 0.4676
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4710 - val_loss: 0.4665
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4750 - val_loss: 0.4690
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4714 - val_loss: 0.4660
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4704 - val_loss: 0.4624
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4707 - val_loss: 0.4642
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4729 - val_loss: 0.4647
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4704 - val_loss: 0.4596
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4674 - val_loss: 0.4636
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4686 - val_loss: 0.4646
Epoch 55/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4661 - val_loss: 0.4670
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4695 - val_loss: 0.4575
Epoch 57/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4659 - val_loss: 0.4584
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4672 - val_loss: 0.4543
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4688 - val_loss: 0.4610
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4646 - val_loss: 0.4630
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4669 - val_loss: 0.4586
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4650 - val_loss: 0.4576
Epoch 63/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4650 - val_loss: 0.4578
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4641 - val_loss: 0.4600
Epoch 65/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4672 - val_loss: 0.4595
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4655 - val_loss: 0.4586
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4649 - val_loss: 0.4563
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4645 - val_loss: 0.4561
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4653 - val_loss: 0.4595
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4636 - val_loss: 0.4574
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4655 - val_loss: 0.4563
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4659 - val_loss: 0.4597
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4631 - val_loss: 0.4571
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4628 - val_loss: 0.4550
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4630 - val_loss: 0.4578
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4624 - val_loss: 0.4561
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4626 - val_loss: 0.4552
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4615 - val_loss: 0.4523
Epoch 79/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4633 - val_loss: 0.4584
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4619 - val_loss: 0.4597
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4613 - val_loss: 0.4534
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4620 - val_loss: 0.4559
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4611 - val_loss: 0.4561
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4595 - val_loss: 0.4548
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4572
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4617 - val_loss: 0.4516
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4522
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4611 - val_loss: 0.4534
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4612 - val_loss: 0.4533
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4610 - val_loss: 0.4553
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4620 - val_loss: 0.4520
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4601 - val_loss: 0.4536
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4608 - val_loss: 0.4495
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4607 - val_loss: 0.4570
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4600 - val_loss: 0.4546
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4586 - val_loss: 0.4539
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4595 - val_loss: 0.4502
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4596 - val_loss: 0.4512
Epoch 99/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4610 - val_loss: 0.4566
Epoch 100/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4596 - val_loss: 0.4548
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 18.06 seconds.
Calculating affinities...
Calculated affinities in 0.34 seconds.
Calculated graph and diffusion operator in 18.41 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 1.14 seconds.
Calculating KMeans...
Calculated KMeans in 30.40 seconds.
Calculated landmark operator in 33.69 seconds.
Calculating optimal t...
Automatically selected t = 38
Calculated optimal t in 4.11 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.87 seconds.
Calculating metric MDS...
Calculated metric MDS in 22.44 seconds.
Calculated PHATE in 79.56 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_395 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_396 (Dense) (None, 32) 2080 dense_395[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_396[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_396[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_397 (Dense) (None, 32) 128
_________________________________________________________________
dense_398 (Dense) (None, 64) 2112
_________________________________________________________________
dense_399 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_395 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_396 (Dense) (None, 32) 2080 dense_395[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_396[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_396[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1310 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1310 [()] 0 tf_op_layer_Shape_1310[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_131 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1310[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_131[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_393 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_131 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_393[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1311 (TensorF [(2,)] 0 tf_op_layer_Add_131[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1313 (TensorF [(2,)] 0 tf_op_layer_Add_131[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1312 (TensorF [(2,)] 0 tf_op_layer_Add_131[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1314 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1316 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1315 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1317 (TensorF [(2,)] 0 tf_op_layer_Add_131[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1319 (TensorF [(2,)] 0 tf_op_layer_Add_131[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1318 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1311 [()] 0 tf_op_layer_Shape_1311[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1313 [()] 0 tf_op_layer_Shape_1313[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1312 [()] 0 tf_op_layer_Shape_1312[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1314 [()] 0 tf_op_layer_Shape_1314[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1316 [()] 0 tf_op_layer_Shape_1316[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1315 [()] 0 tf_op_layer_Shape_1315[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1317 [()] 0 tf_op_layer_Shape_1317[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1319 [()] 0 tf_op_layer_Shape_1319[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1318 [()] 0 tf_op_layer_Shape_1318[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_786/shape ( [(3,)] 0 tf_op_layer_strided_slice_1311[0]
tf_op_layer_strided_slice_1313[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_787/shape ( [(3,)] 0 tf_op_layer_strided_slice_1312[0]
tf_op_layer_strided_slice_1313[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_788/shape ( [(3,)] 0 tf_op_layer_strided_slice_1314[0]
tf_op_layer_strided_slice_1316[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_789/shape ( [(3,)] 0 tf_op_layer_strided_slice_1315[0]
tf_op_layer_strided_slice_1316[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_790/shape ( [(3,)] 0 tf_op_layer_strided_slice_1317[0]
tf_op_layer_strided_slice_1319[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_791/shape ( [(3,)] 0 tf_op_layer_strided_slice_1318[0]
tf_op_layer_strided_slice_1319[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_786 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_131[0][0]
tf_op_layer_Reshape_786/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_786/multiples [(3,)] 0 tf_op_layer_strided_slice_1312[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_787 (Tensor [(1, None, None)] 0 tf_op_layer_Add_131[0][0]
tf_op_layer_Reshape_787/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_787/multiples [(3,)] 0 tf_op_layer_strided_slice_1311[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_788 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_788/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_788/multiples [(3,)] 0 tf_op_layer_strided_slice_1315[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_789 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_789/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_789/multiples [(3,)] 0 tf_op_layer_strided_slice_1314[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_790 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_131[0][0]
tf_op_layer_Reshape_790/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_790/multiples [(3,)] 0 tf_op_layer_strided_slice_1318[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_791 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_791/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_791/multiples [(3,)] 0 tf_op_layer_strided_slice_1317[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_786 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_786[0][0]
tf_op_layer_Tile_786/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_787 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_787[0][0]
tf_op_layer_Tile_787/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_788 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_788[0][0]
tf_op_layer_Tile_788/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_789 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_789[0][0]
tf_op_layer_Tile_789/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_790 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_790[0][0]
tf_op_layer_Tile_790/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_791 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_791[0][0]
tf_op_layer_Tile_791/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_655 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_786[0][0]
tf_op_layer_Tile_787[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_656 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_788[0][0]
tf_op_layer_Tile_789[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_657 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_790[0][0]
tf_op_layer_Tile_791[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_524 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_655[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_525 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_656[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_526 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_657[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_917 (TensorFlo [(None, None)] 0 tf_op_layer_Square_524[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_918 (TensorFlo [(None, None)] 0 tf_op_layer_Square_525[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_919 (TensorFlo [(None, None)] 0 tf_op_layer_Square_526[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_393 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_917[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_393 (TensorFlo [()] 0 tf_op_layer_strided_slice_1313[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_394 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_918[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_394 (TensorFlo [()] 0 tf_op_layer_strided_slice_1316[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_395 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_919[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_395 (TensorFlo [()] 0 tf_op_layer_strided_slice_1319[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_393 (Tensor [(None, None)] 0 tf_op_layer_Neg_393[0][0]
tf_op_layer_Cast_393[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_394 (Tensor [(None, None)] 0 tf_op_layer_Neg_394[0][0]
tf_op_layer_Cast_394[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_395 (Tensor [(None, None)] 0 tf_op_layer_Neg_395[0][0]
tf_op_layer_Cast_395[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_393 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_393[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_394 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_394[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_395 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_395[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_920 (TensorFlo [()] 0 tf_op_layer_Exp_393[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_921 (TensorFlo [()] 0 tf_op_layer_Exp_394[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_922 (TensorFlo [()] 0 tf_op_layer_Exp_395[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_659 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_262 (TensorFl [()] 0 tf_op_layer_Mean_920[0][0]
tf_op_layer_Mean_921[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_394 (TensorFlow [()] 0 tf_op_layer_Mean_922[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_527 (TensorF [(None, 97)] 0 tf_op_layer_Sub_659[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_658 (TensorFlow [()] 0 tf_op_layer_AddV2_262[0][0]
tf_op_layer_Mul_394[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_131 (TensorFlow [(None,)] 0 tf_op_layer_Square_527[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_395 (TensorFlow [()] 0 tf_op_layer_Sub_658[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_263 (TensorFl [(None,)] 0 tf_op_layer_Sum_131[0][0]
tf_op_layer_Mul_395[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_923 (TensorFlo [()] 0 tf_op_layer_AddV2_263[0][0]
__________________________________________________________________________________________________
add_loss_131 (AddLoss) () 0 tf_op_layer_Mean_923[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 13s - loss: 8.0332WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0022s vs `on_train_batch_end` time: 0.1225s). Check your callbacks.
218/218 [==============================] - 1s 3ms/step - loss: 1.0017 - val_loss: 0.4515
Epoch 2/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4205 - val_loss: 0.3888
Epoch 3/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3948 - val_loss: 0.3799
Epoch 4/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3871 - val_loss: 0.3812
Epoch 5/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3845 - val_loss: 0.3694
Epoch 6/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3810 - val_loss: 0.3702
Epoch 7/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3778 - val_loss: 0.3683
Epoch 8/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3760 - val_loss: 0.3655
Epoch 9/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3758 - val_loss: 0.3671
Epoch 10/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3740 - val_loss: 0.3653
Epoch 11/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3733 - val_loss: 0.3584
Epoch 12/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3733 - val_loss: 0.3620
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3703 - val_loss: 0.3628
Epoch 14/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3686 - val_loss: 0.3590
Epoch 15/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3698 - val_loss: 0.3579
Epoch 16/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3661 - val_loss: 0.3590
Epoch 17/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3672 - val_loss: 0.3565
Epoch 18/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3655 - val_loss: 0.3553
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3653 - val_loss: 0.3600
Epoch 20/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3639 - val_loss: 0.3553
Epoch 21/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3640 - val_loss: 0.3526
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3619 - val_loss: 0.3543
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3637 - val_loss: 0.3533
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3610 - val_loss: 0.3507
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3620 - val_loss: 0.3521
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3612 - val_loss: 0.3519
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3607 - val_loss: 0.3499
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3594 - val_loss: 0.3534
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3599 - val_loss: 0.3522
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3598 - val_loss: 0.3484
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3577 - val_loss: 0.3496
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3575 - val_loss: 0.3521
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3572 - val_loss: 0.3486
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3562 - val_loss: 0.3464
Epoch 35/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3559 - val_loss: 0.3479
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3560 - val_loss: 0.3466
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3539 - val_loss: 0.3464
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3557 - val_loss: 0.3472
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3547 - val_loss: 0.3487
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3552 - val_loss: 0.3445
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3547 - val_loss: 0.3456
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3539 - val_loss: 0.3469
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3459
Epoch 44/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3540 - val_loss: 0.3464
Epoch 45/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3534 - val_loss: 0.3445
Epoch 46/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3531 - val_loss: 0.3442
Epoch 47/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3521 - val_loss: 0.3424
Epoch 48/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3520 - val_loss: 0.3441
Epoch 49/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3512 - val_loss: 0.3446
Epoch 50/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3517 - val_loss: 0.3449
Epoch 51/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3499 - val_loss: 0.3470
Epoch 52/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3522 - val_loss: 0.3442
Epoch 53/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3503 - val_loss: 0.3418
Epoch 54/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3506 - val_loss: 0.3427
Epoch 55/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3514 - val_loss: 0.3436
Epoch 56/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3506 - val_loss: 0.3429
Epoch 57/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3507 - val_loss: 0.3422
Epoch 58/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3501 - val_loss: 0.3469
Epoch 59/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3505 - val_loss: 0.3411
Epoch 60/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3505 - val_loss: 0.3403
Epoch 61/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3493 - val_loss: 0.3416
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3500 - val_loss: 0.3401
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3500 - val_loss: 0.3423
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3498 - val_loss: 0.3411
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3502 - val_loss: 0.3426
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3502 - val_loss: 0.3421
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3448
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3393
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3492 - val_loss: 0.3392
Epoch 70/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3487 - val_loss: 0.3385
Epoch 71/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3489 - val_loss: 0.3395
Epoch 72/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3487 - val_loss: 0.3408
Epoch 73/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3477 - val_loss: 0.3413
Epoch 74/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3491 - val_loss: 0.3404
Epoch 75/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3482 - val_loss: 0.3401
Epoch 76/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3471 - val_loss: 0.3426
Epoch 77/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3483 - val_loss: 0.3403
Epoch 78/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3490 - val_loss: 0.3406
Epoch 79/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3471 - val_loss: 0.3414
Epoch 80/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3483 - val_loss: 0.3400
Epoch 81/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3476 - val_loss: 0.3392
Epoch 82/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3473 - val_loss: 0.3403
Epoch 83/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3475 - val_loss: 0.3399
Epoch 84/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3473 - val_loss: 0.3402
Epoch 85/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3455 - val_loss: 0.3392
Epoch 86/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3460 - val_loss: 0.3399
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3390
Epoch 88/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3461 - val_loss: 0.3380
Epoch 89/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3470 - val_loss: 0.3409
Epoch 90/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3477 - val_loss: 0.3359
Epoch 91/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3471 - val_loss: 0.3377
Epoch 92/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3471 - val_loss: 0.3390
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3458 - val_loss: 0.3394
Epoch 94/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3461 - val_loss: 0.3401
Epoch 95/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3451 - val_loss: 0.3387
Epoch 96/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3463 - val_loss: 0.3384
Epoch 97/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3463 - val_loss: 0.3361
Epoch 98/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3471 - val_loss: 0.3370
Epoch 99/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3456 - val_loss: 0.3371
Epoch 100/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3461 - val_loss: 0.3375
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_400 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1320 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1320 [()] 0 tf_op_layer_Shape_1320[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_132 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1320[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_132[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_396 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_132 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_396[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1321 (TensorF [(2,)] 0 tf_op_layer_Add_132[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1323 (TensorF [(2,)] 0 tf_op_layer_Add_132[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1322 (TensorF [(2,)] 0 tf_op_layer_Add_132[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1324 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1326 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1325 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1327 (TensorF [(2,)] 0 tf_op_layer_Add_132[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1329 (TensorF [(2,)] 0 tf_op_layer_Add_132[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1328 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1321 [()] 0 tf_op_layer_Shape_1321[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1323 [()] 0 tf_op_layer_Shape_1323[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1322 [()] 0 tf_op_layer_Shape_1322[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1324 [()] 0 tf_op_layer_Shape_1324[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1326 [()] 0 tf_op_layer_Shape_1326[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1325 [()] 0 tf_op_layer_Shape_1325[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1327 [()] 0 tf_op_layer_Shape_1327[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1329 [()] 0 tf_op_layer_Shape_1329[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1328 [()] 0 tf_op_layer_Shape_1328[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_792/shape ( [(3,)] 0 tf_op_layer_strided_slice_1321[0]
tf_op_layer_strided_slice_1323[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_793/shape ( [(3,)] 0 tf_op_layer_strided_slice_1322[0]
tf_op_layer_strided_slice_1323[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_794/shape ( [(3,)] 0 tf_op_layer_strided_slice_1324[0]
tf_op_layer_strided_slice_1326[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_795/shape ( [(3,)] 0 tf_op_layer_strided_slice_1325[0]
tf_op_layer_strided_slice_1326[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_796/shape ( [(3,)] 0 tf_op_layer_strided_slice_1327[0]
tf_op_layer_strided_slice_1329[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_797/shape ( [(3,)] 0 tf_op_layer_strided_slice_1328[0]
tf_op_layer_strided_slice_1329[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_792 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_132[0][0]
tf_op_layer_Reshape_792/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_792/multiples [(3,)] 0 tf_op_layer_strided_slice_1322[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_793 (Tensor [(1, None, None)] 0 tf_op_layer_Add_132[0][0]
tf_op_layer_Reshape_793/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_793/multiples [(3,)] 0 tf_op_layer_strided_slice_1321[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_794 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_794/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_794/multiples [(3,)] 0 tf_op_layer_strided_slice_1325[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_795 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_795/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_795/multiples [(3,)] 0 tf_op_layer_strided_slice_1324[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_796 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_132[0][0]
tf_op_layer_Reshape_796/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_796/multiples [(3,)] 0 tf_op_layer_strided_slice_1328[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_797 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_797/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_797/multiples [(3,)] 0 tf_op_layer_strided_slice_1327[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_792 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_792[0][0]
tf_op_layer_Tile_792/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_793 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_793[0][0]
tf_op_layer_Tile_793/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_794 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_794[0][0]
tf_op_layer_Tile_794/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_795 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_795[0][0]
tf_op_layer_Tile_795/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_796 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_796[0][0]
tf_op_layer_Tile_796/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_797 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_797[0][0]
tf_op_layer_Tile_797/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_660 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_792[0][0]
tf_op_layer_Tile_793[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_661 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_794[0][0]
tf_op_layer_Tile_795[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_662 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_796[0][0]
tf_op_layer_Tile_797[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_528 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_660[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_529 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_661[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_530 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_662[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_924 (TensorFlo [(None, None)] 0 tf_op_layer_Square_528[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_925 (TensorFlo [(None, None)] 0 tf_op_layer_Square_529[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_926 (TensorFlo [(None, None)] 0 tf_op_layer_Square_530[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_396 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_924[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_396 (TensorFlo [()] 0 tf_op_layer_strided_slice_1323[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_397 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_925[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_397 (TensorFlo [()] 0 tf_op_layer_strided_slice_1326[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_398 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_926[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_398 (TensorFlo [()] 0 tf_op_layer_strided_slice_1329[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_396 (Tensor [(None, None)] 0 tf_op_layer_Neg_396[0][0]
tf_op_layer_Cast_396[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_397 (Tensor [(None, None)] 0 tf_op_layer_Neg_397[0][0]
tf_op_layer_Cast_397[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_398 (Tensor [(None, None)] 0 tf_op_layer_Neg_398[0][0]
tf_op_layer_Cast_398[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_396 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_396[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_397 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_397[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_398 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_398[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_927 (TensorFlo [()] 0 tf_op_layer_Exp_396[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_928 (TensorFlo [()] 0 tf_op_layer_Exp_397[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_929 (TensorFlo [()] 0 tf_op_layer_Exp_398[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_664 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_264 (TensorFl [()] 0 tf_op_layer_Mean_927[0][0]
tf_op_layer_Mean_928[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_397 (TensorFlow [()] 0 tf_op_layer_Mean_929[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_531 (TensorF [(None, 97)] 0 tf_op_layer_Sub_664[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_663 (TensorFlow [()] 0 tf_op_layer_AddV2_264[0][0]
tf_op_layer_Mul_397[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_132 (TensorFlow [(None,)] 0 tf_op_layer_Square_531[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_398 (TensorFlow [()] 0 tf_op_layer_Sub_663[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_265 (TensorFl [(None,)] 0 tf_op_layer_Sum_132[0][0]
tf_op_layer_Mul_398[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_930 (TensorFlo [()] 0 tf_op_layer_AddV2_265[0][0]
__________________________________________________________________________________________________
add_loss_132 (AddLoss) () 0 tf_op_layer_Mean_930[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 23s - loss: 8.1048WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.2127s). Check your callbacks.
218/218 [==============================] - 1s 5ms/step - loss: 2.0937 - val_loss: 0.9999
Epoch 2/100
218/218 [==============================] - 0s 2ms/step - loss: 0.8430 - val_loss: 0.7669
Epoch 3/100
218/218 [==============================] - 0s 2ms/step - loss: 0.6976 - val_loss: 0.6826
Epoch 4/100
218/218 [==============================] - 0s 2ms/step - loss: 0.6368 - val_loss: 0.6354
Epoch 5/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5967 - val_loss: 0.6053
Epoch 6/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5788 - val_loss: 0.5879
Epoch 7/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5629 - val_loss: 0.5829
Epoch 8/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5514 - val_loss: 0.5705
Epoch 9/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5439 - val_loss: 0.5591
Epoch 10/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5344 - val_loss: 0.5450
Epoch 11/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5333 - val_loss: 0.5422
Epoch 12/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5248 - val_loss: 0.5386
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5197 - val_loss: 0.5399
Epoch 14/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5167 - val_loss: 0.5273
Epoch 15/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5135 - val_loss: 0.5228
Epoch 16/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5090 - val_loss: 0.5272
Epoch 17/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5078 - val_loss: 0.5230
Epoch 18/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5013 - val_loss: 0.5182
Epoch 19/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4996 - val_loss: 0.5196
Epoch 20/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5015 - val_loss: 0.5131
Epoch 21/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4971 - val_loss: 0.5135
Epoch 22/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4975 - val_loss: 0.5165
Epoch 23/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4945 - val_loss: 0.5078
Epoch 24/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4916 - val_loss: 0.5095
Epoch 25/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4903 - val_loss: 0.5081
Epoch 26/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4867 - val_loss: 0.5062
Epoch 27/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4879 - val_loss: 0.5030
Epoch 28/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4862 - val_loss: 0.5054
Epoch 29/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4850 - val_loss: 0.5000
Epoch 30/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4840 - val_loss: 0.4971
Epoch 31/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4832 - val_loss: 0.4980
Epoch 32/100
218/218 [==============================] - 1s 3ms/step - loss: 0.4811 - val_loss: 0.4952
Epoch 33/100
218/218 [==============================] - 1s 3ms/step - loss: 0.4811 - val_loss: 0.4982
Epoch 34/100
218/218 [==============================] - 1s 3ms/step - loss: 0.4800 - val_loss: 0.4946
Epoch 35/100
218/218 [==============================] - 1s 3ms/step - loss: 0.4804 - val_loss: 0.4962
Epoch 36/100
218/218 [==============================] - 1s 3ms/step - loss: 0.4795 - val_loss: 0.4905
Epoch 37/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4775 - val_loss: 0.4978
Epoch 38/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4781 - val_loss: 0.4986
Epoch 39/100
218/218 [==============================] - 1s 3ms/step - loss: 0.4778 - val_loss: 0.4936
Epoch 40/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4771 - val_loss: 0.4944
Epoch 41/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4775 - val_loss: 0.4949
Epoch 42/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4738 - val_loss: 0.4936
Epoch 43/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4759 - val_loss: 0.4896
Epoch 44/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4745 - val_loss: 0.4907
Epoch 45/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4727 - val_loss: 0.4940
Epoch 46/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4746 - val_loss: 0.4901
Epoch 47/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4735 - val_loss: 0.4886
Epoch 48/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4717 - val_loss: 0.4911
Epoch 49/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4716 - val_loss: 0.4911
Epoch 50/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4707 - val_loss: 0.4838
Epoch 51/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4709 - val_loss: 0.4872
Epoch 52/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4704 - val_loss: 0.4864
Epoch 53/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4701 - val_loss: 0.4856
Epoch 54/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4720 - val_loss: 0.4875
Epoch 55/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4691 - val_loss: 0.4853
Epoch 56/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4692 - val_loss: 0.4925
Epoch 57/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4697 - val_loss: 0.4878
Epoch 58/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4693 - val_loss: 0.4858
Epoch 59/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4668 - val_loss: 0.4841
Epoch 60/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4686 - val_loss: 0.4865
Epoch 61/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4695 - val_loss: 0.4882
Epoch 62/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4697 - val_loss: 0.4848
Epoch 63/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4652 - val_loss: 0.4823
Epoch 64/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4695 - val_loss: 0.4843
Epoch 65/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4670 - val_loss: 0.4797
Epoch 66/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4664 - val_loss: 0.4828
Epoch 67/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4654 - val_loss: 0.4798
Epoch 68/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4648 - val_loss: 0.4847
Epoch 69/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4661 - val_loss: 0.4866
Epoch 70/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4649 - val_loss: 0.4838
Epoch 71/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4646 - val_loss: 0.4823
Epoch 72/100
218/218 [==============================] - 1s 2ms/step - loss: 0.4668 - val_loss: 0.4856
Epoch 73/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4631 - val_loss: 0.4820
Epoch 74/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4655 - val_loss: 0.4821
Epoch 75/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4638 - val_loss: 0.4804
Epoch 76/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4644 - val_loss: 0.4795
Epoch 77/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4635 - val_loss: 0.4800
Epoch 78/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4666 - val_loss: 0.4832
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4655 - val_loss: 0.4831
Epoch 80/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4647 - val_loss: 0.4805
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4619 - val_loss: 0.4822
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4636 - val_loss: 0.4816
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4616 - val_loss: 0.4792
Epoch 84/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4633 - val_loss: 0.4795
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4620 - val_loss: 0.4814
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4629 - val_loss: 0.4758
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4636 - val_loss: 0.4805
Epoch 88/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4617 - val_loss: 0.4774
Epoch 89/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4639 - val_loss: 0.4775
Epoch 90/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4636 - val_loss: 0.4762
Epoch 91/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4623 - val_loss: 0.4809
Epoch 92/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4619 - val_loss: 0.4822
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4624 - val_loss: 0.4795
Epoch 94/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4615 - val_loss: 0.4800
Epoch 95/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4620 - val_loss: 0.4782
Epoch 96/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4610 - val_loss: 0.4774
Epoch 97/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4620 - val_loss: 0.4796
Epoch 98/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4611 - val_loss: 0.4780
Epoch 99/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4602 - val_loss: 0.4767
Epoch 100/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4600 - val_loss: 0.4752
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 24.82 seconds.
Calculating affinities...
Calculated affinities in 0.33 seconds.
Calculated graph and diffusion operator in 25.18 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 1.19 seconds.
Calculating KMeans...
Calculated KMeans in 27.28 seconds.
Calculated landmark operator in 30.85 seconds.
Calculating optimal t...
Automatically selected t = 28
Calculated optimal t in 5.46 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.64 seconds.
Calculating metric MDS...
Calculated metric MDS in 23.89 seconds.
Calculated PHATE in 86.03 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_401 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_402 (Dense) (None, 32) 2080 dense_401[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_402[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_402[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_403 (Dense) (None, 32) 128
_________________________________________________________________
dense_404 (Dense) (None, 64) 2112
_________________________________________________________________
dense_405 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_401 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_402 (Dense) (None, 32) 2080 dense_401[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_402[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_402[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1330 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1330 [()] 0 tf_op_layer_Shape_1330[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_133 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1330[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_133[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_399 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_133 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_399[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1331 (TensorF [(2,)] 0 tf_op_layer_Add_133[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1333 (TensorF [(2,)] 0 tf_op_layer_Add_133[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1332 (TensorF [(2,)] 0 tf_op_layer_Add_133[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1334 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1336 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1335 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1337 (TensorF [(2,)] 0 tf_op_layer_Add_133[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1339 (TensorF [(2,)] 0 tf_op_layer_Add_133[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1338 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1331 [()] 0 tf_op_layer_Shape_1331[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1333 [()] 0 tf_op_layer_Shape_1333[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1332 [()] 0 tf_op_layer_Shape_1332[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1334 [()] 0 tf_op_layer_Shape_1334[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1336 [()] 0 tf_op_layer_Shape_1336[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1335 [()] 0 tf_op_layer_Shape_1335[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1337 [()] 0 tf_op_layer_Shape_1337[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1339 [()] 0 tf_op_layer_Shape_1339[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1338 [()] 0 tf_op_layer_Shape_1338[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_798/shape ( [(3,)] 0 tf_op_layer_strided_slice_1331[0]
tf_op_layer_strided_slice_1333[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_799/shape ( [(3,)] 0 tf_op_layer_strided_slice_1332[0]
tf_op_layer_strided_slice_1333[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_800/shape ( [(3,)] 0 tf_op_layer_strided_slice_1334[0]
tf_op_layer_strided_slice_1336[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_801/shape ( [(3,)] 0 tf_op_layer_strided_slice_1335[0]
tf_op_layer_strided_slice_1336[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_802/shape ( [(3,)] 0 tf_op_layer_strided_slice_1337[0]
tf_op_layer_strided_slice_1339[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_803/shape ( [(3,)] 0 tf_op_layer_strided_slice_1338[0]
tf_op_layer_strided_slice_1339[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_798 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_133[0][0]
tf_op_layer_Reshape_798/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_798/multiples [(3,)] 0 tf_op_layer_strided_slice_1332[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_799 (Tensor [(1, None, None)] 0 tf_op_layer_Add_133[0][0]
tf_op_layer_Reshape_799/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_799/multiples [(3,)] 0 tf_op_layer_strided_slice_1331[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_800 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_800/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_800/multiples [(3,)] 0 tf_op_layer_strided_slice_1335[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_801 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_801/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_801/multiples [(3,)] 0 tf_op_layer_strided_slice_1334[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_802 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_133[0][0]
tf_op_layer_Reshape_802/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_802/multiples [(3,)] 0 tf_op_layer_strided_slice_1338[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_803 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_803/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_803/multiples [(3,)] 0 tf_op_layer_strided_slice_1337[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_798 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_798[0][0]
tf_op_layer_Tile_798/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_799 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_799[0][0]
tf_op_layer_Tile_799/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_800 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_800[0][0]
tf_op_layer_Tile_800/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_801 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_801[0][0]
tf_op_layer_Tile_801/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_802 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_802[0][0]
tf_op_layer_Tile_802/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_803 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_803[0][0]
tf_op_layer_Tile_803/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_665 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_798[0][0]
tf_op_layer_Tile_799[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_666 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_800[0][0]
tf_op_layer_Tile_801[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_667 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_802[0][0]
tf_op_layer_Tile_803[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_532 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_665[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_533 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_666[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_534 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_667[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_931 (TensorFlo [(None, None)] 0 tf_op_layer_Square_532[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_932 (TensorFlo [(None, None)] 0 tf_op_layer_Square_533[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_933 (TensorFlo [(None, None)] 0 tf_op_layer_Square_534[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_399 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_931[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_399 (TensorFlo [()] 0 tf_op_layer_strided_slice_1333[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_400 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_932[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_400 (TensorFlo [()] 0 tf_op_layer_strided_slice_1336[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_401 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_933[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_401 (TensorFlo [()] 0 tf_op_layer_strided_slice_1339[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_399 (Tensor [(None, None)] 0 tf_op_layer_Neg_399[0][0]
tf_op_layer_Cast_399[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_400 (Tensor [(None, None)] 0 tf_op_layer_Neg_400[0][0]
tf_op_layer_Cast_400[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_401 (Tensor [(None, None)] 0 tf_op_layer_Neg_401[0][0]
tf_op_layer_Cast_401[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_399 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_399[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_400 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_400[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_401 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_401[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_934 (TensorFlo [()] 0 tf_op_layer_Exp_399[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_935 (TensorFlo [()] 0 tf_op_layer_Exp_400[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_936 (TensorFlo [()] 0 tf_op_layer_Exp_401[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_669 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_266 (TensorFl [()] 0 tf_op_layer_Mean_934[0][0]
tf_op_layer_Mean_935[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_400 (TensorFlow [()] 0 tf_op_layer_Mean_936[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_535 (TensorF [(None, 97)] 0 tf_op_layer_Sub_669[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_668 (TensorFlow [()] 0 tf_op_layer_AddV2_266[0][0]
tf_op_layer_Mul_400[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_133 (TensorFlow [(None,)] 0 tf_op_layer_Square_535[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_401 (TensorFlow [()] 0 tf_op_layer_Sub_668[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_267 (TensorFl [(None,)] 0 tf_op_layer_Sum_133[0][0]
tf_op_layer_Mul_401[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_937 (TensorFlo [()] 0 tf_op_layer_AddV2_267[0][0]
__________________________________________________________________________________________________
add_loss_133 (AddLoss) () 0 tf_op_layer_Mean_937[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 21s - loss: 8.2836WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0017s vs `on_train_batch_end` time: 0.1899s). Check your callbacks.
218/218 [==============================] - 1s 4ms/step - loss: 0.9276 - val_loss: 0.5225
Epoch 2/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4723 - val_loss: 0.4408
Epoch 3/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3968 - val_loss: 0.4032
Epoch 4/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3806 - val_loss: 0.3943
Epoch 5/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3738 - val_loss: 0.3859
Epoch 6/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3680 - val_loss: 0.3815
Epoch 7/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3666 - val_loss: 0.3851
Epoch 8/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3637 - val_loss: 0.3791
Epoch 9/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3614 - val_loss: 0.3783
Epoch 10/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3599 - val_loss: 0.3769
Epoch 11/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3591 - val_loss: 0.3744
Epoch 12/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3561 - val_loss: 0.3742
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3567 - val_loss: 0.3750
Epoch 14/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3548 - val_loss: 0.3718
Epoch 15/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3549 - val_loss: 0.3705
Epoch 16/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3525 - val_loss: 0.3699
Epoch 17/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3535 - val_loss: 0.3717
Epoch 18/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3539 - val_loss: 0.3702
Epoch 19/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3518 - val_loss: 0.3689
Epoch 20/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3508 - val_loss: 0.3677
Epoch 21/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3503 - val_loss: 0.3710
Epoch 22/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3504 - val_loss: 0.3685
Epoch 23/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3498 - val_loss: 0.3654
Epoch 24/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3497 - val_loss: 0.3689
Epoch 25/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3490 - val_loss: 0.3655
Epoch 26/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3503 - val_loss: 0.3651
Epoch 27/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3493 - val_loss: 0.3661
Epoch 28/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3471 - val_loss: 0.3667
Epoch 29/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3477 - val_loss: 0.3669
Epoch 30/100
218/218 [==============================] - 1s 2ms/step - loss: 0.3474 - val_loss: 0.3658
Epoch 31/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3468 - val_loss: 0.3625
Epoch 32/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3469 - val_loss: 0.3671
Epoch 33/100
218/218 [==============================] - 1s 2ms/step - loss: 0.3472 - val_loss: 0.3636
Epoch 34/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3467 - val_loss: 0.3647
Epoch 35/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3473 - val_loss: 0.3642
Epoch 36/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3463 - val_loss: 0.3675
Epoch 37/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3462 - val_loss: 0.3641
Epoch 38/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3461 - val_loss: 0.3653
Epoch 39/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3455 - val_loss: 0.3627
Epoch 40/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3446 - val_loss: 0.3641
Epoch 41/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3452 - val_loss: 0.3657
Epoch 42/100
218/218 [==============================] - 1s 3ms/step - loss: 0.3450 - val_loss: 0.3627
Epoch 43/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3446 - val_loss: 0.3616
Epoch 44/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3440 - val_loss: 0.3639
Epoch 45/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3451 - val_loss: 0.3644
Epoch 46/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3441 - val_loss: 0.3647
Epoch 47/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3435 - val_loss: 0.3625
Epoch 48/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3433 - val_loss: 0.3617
Epoch 49/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3426 - val_loss: 0.3615
Epoch 50/100
218/218 [==============================] - 1s 3ms/step - loss: 0.3439 - val_loss: 0.3627
Epoch 51/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3451 - val_loss: 0.3628
Epoch 52/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3435 - val_loss: 0.3644
Epoch 53/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3439 - val_loss: 0.3637
Epoch 54/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3436 - val_loss: 0.3609
Epoch 55/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3434 - val_loss: 0.3584
Epoch 56/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3439 - val_loss: 0.3619
Epoch 57/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3419 - val_loss: 0.3609
Epoch 58/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3424 - val_loss: 0.3579
Epoch 59/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3420 - val_loss: 0.3593
Epoch 60/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3422 - val_loss: 0.3597
Epoch 61/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3422 - val_loss: 0.3608
Epoch 62/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3435 - val_loss: 0.3607
Epoch 63/100
218/218 [==============================] - 1s 2ms/step - loss: 0.3421 - val_loss: 0.3611
Epoch 64/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3419 - val_loss: 0.3585
Epoch 65/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3418 - val_loss: 0.3588
Epoch 66/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3417 - val_loss: 0.3608
Epoch 67/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3410 - val_loss: 0.3575
Epoch 68/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3418 - val_loss: 0.3609
Epoch 69/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3411 - val_loss: 0.3577
Epoch 70/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3415 - val_loss: 0.3599
Epoch 71/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3416 - val_loss: 0.3598
Epoch 72/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3406 - val_loss: 0.3587
Epoch 73/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3409 - val_loss: 0.3602
Epoch 74/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3403 - val_loss: 0.3631
Epoch 75/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3411 - val_loss: 0.3589
Epoch 76/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3407 - val_loss: 0.3582
Epoch 77/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3405 - val_loss: 0.3602
Epoch 78/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3406 - val_loss: 0.3600
Epoch 79/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3403 - val_loss: 0.3587
Epoch 80/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3408 - val_loss: 0.3598
Epoch 81/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3395 - val_loss: 0.3587
Epoch 82/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3410 - val_loss: 0.3603
Epoch 83/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3396 - val_loss: 0.3588
Epoch 84/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3393 - val_loss: 0.3562
Epoch 85/100
218/218 [==============================] - 1s 2ms/step - loss: 0.3394 - val_loss: 0.3604
Epoch 86/100
218/218 [==============================] - 1s 2ms/step - loss: 0.3400 - val_loss: 0.3573
Epoch 87/100
218/218 [==============================] - 1s 3ms/step - loss: 0.3399 - val_loss: 0.3566
Epoch 88/100
218/218 [==============================] - 1s 2ms/step - loss: 0.3397 - val_loss: 0.3578
Epoch 89/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3397 - val_loss: 0.3601
Epoch 90/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3401 - val_loss: 0.3595
Epoch 91/100
218/218 [==============================] - 1s 2ms/step - loss: 0.3393 - val_loss: 0.3575
Epoch 92/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3399 - val_loss: 0.3566
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3385 - val_loss: 0.3579
Epoch 94/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3390 - val_loss: 0.3587
Epoch 95/100
218/218 [==============================] - 1s 3ms/step - loss: 0.3384 - val_loss: 0.3596
Epoch 96/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3390 - val_loss: 0.3591
Epoch 97/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3398 - val_loss: 0.3565
Epoch 98/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3394 - val_loss: 0.3573
Epoch 99/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3392 - val_loss: 0.3586
Epoch 100/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3400 - val_loss: 0.3607
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_406 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1340 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1340 [()] 0 tf_op_layer_Shape_1340[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_134 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1340[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_134[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_402 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_134 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_402[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1341 (TensorF [(2,)] 0 tf_op_layer_Add_134[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1343 (TensorF [(2,)] 0 tf_op_layer_Add_134[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1342 (TensorF [(2,)] 0 tf_op_layer_Add_134[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1344 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1346 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1345 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1347 (TensorF [(2,)] 0 tf_op_layer_Add_134[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1349 (TensorF [(2,)] 0 tf_op_layer_Add_134[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1348 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1341 [()] 0 tf_op_layer_Shape_1341[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1343 [()] 0 tf_op_layer_Shape_1343[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1342 [()] 0 tf_op_layer_Shape_1342[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1344 [()] 0 tf_op_layer_Shape_1344[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1346 [()] 0 tf_op_layer_Shape_1346[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1345 [()] 0 tf_op_layer_Shape_1345[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1347 [()] 0 tf_op_layer_Shape_1347[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1349 [()] 0 tf_op_layer_Shape_1349[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1348 [()] 0 tf_op_layer_Shape_1348[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_804/shape ( [(3,)] 0 tf_op_layer_strided_slice_1341[0]
tf_op_layer_strided_slice_1343[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_805/shape ( [(3,)] 0 tf_op_layer_strided_slice_1342[0]
tf_op_layer_strided_slice_1343[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_806/shape ( [(3,)] 0 tf_op_layer_strided_slice_1344[0]
tf_op_layer_strided_slice_1346[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_807/shape ( [(3,)] 0 tf_op_layer_strided_slice_1345[0]
tf_op_layer_strided_slice_1346[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_808/shape ( [(3,)] 0 tf_op_layer_strided_slice_1347[0]
tf_op_layer_strided_slice_1349[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_809/shape ( [(3,)] 0 tf_op_layer_strided_slice_1348[0]
tf_op_layer_strided_slice_1349[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_804 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_134[0][0]
tf_op_layer_Reshape_804/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_804/multiples [(3,)] 0 tf_op_layer_strided_slice_1342[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_805 (Tensor [(1, None, None)] 0 tf_op_layer_Add_134[0][0]
tf_op_layer_Reshape_805/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_805/multiples [(3,)] 0 tf_op_layer_strided_slice_1341[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_806 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_806/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_806/multiples [(3,)] 0 tf_op_layer_strided_slice_1345[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_807 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_807/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_807/multiples [(3,)] 0 tf_op_layer_strided_slice_1344[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_808 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_134[0][0]
tf_op_layer_Reshape_808/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_808/multiples [(3,)] 0 tf_op_layer_strided_slice_1348[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_809 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_809/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_809/multiples [(3,)] 0 tf_op_layer_strided_slice_1347[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_804 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_804[0][0]
tf_op_layer_Tile_804/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_805 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_805[0][0]
tf_op_layer_Tile_805/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_806 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_806[0][0]
tf_op_layer_Tile_806/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_807 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_807[0][0]
tf_op_layer_Tile_807/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_808 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_808[0][0]
tf_op_layer_Tile_808/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_809 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_809[0][0]
tf_op_layer_Tile_809/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_670 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_804[0][0]
tf_op_layer_Tile_805[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_671 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_806[0][0]
tf_op_layer_Tile_807[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_672 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_808[0][0]
tf_op_layer_Tile_809[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_536 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_670[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_537 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_671[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_538 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_672[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_938 (TensorFlo [(None, None)] 0 tf_op_layer_Square_536[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_939 (TensorFlo [(None, None)] 0 tf_op_layer_Square_537[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_940 (TensorFlo [(None, None)] 0 tf_op_layer_Square_538[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_402 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_938[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_402 (TensorFlo [()] 0 tf_op_layer_strided_slice_1343[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_403 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_939[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_403 (TensorFlo [()] 0 tf_op_layer_strided_slice_1346[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_404 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_940[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_404 (TensorFlo [()] 0 tf_op_layer_strided_slice_1349[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_402 (Tensor [(None, None)] 0 tf_op_layer_Neg_402[0][0]
tf_op_layer_Cast_402[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_403 (Tensor [(None, None)] 0 tf_op_layer_Neg_403[0][0]
tf_op_layer_Cast_403[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_404 (Tensor [(None, None)] 0 tf_op_layer_Neg_404[0][0]
tf_op_layer_Cast_404[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_402 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_402[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_403 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_403[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_404 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_404[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_941 (TensorFlo [()] 0 tf_op_layer_Exp_402[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_942 (TensorFlo [()] 0 tf_op_layer_Exp_403[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_943 (TensorFlo [()] 0 tf_op_layer_Exp_404[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_674 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_268 (TensorFl [()] 0 tf_op_layer_Mean_941[0][0]
tf_op_layer_Mean_942[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_403 (TensorFlow [()] 0 tf_op_layer_Mean_943[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_539 (TensorF [(None, 97)] 0 tf_op_layer_Sub_674[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_673 (TensorFlow [()] 0 tf_op_layer_AddV2_268[0][0]
tf_op_layer_Mul_403[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_134 (TensorFlow [(None,)] 0 tf_op_layer_Square_539[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_404 (TensorFlow [()] 0 tf_op_layer_Sub_673[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_269 (TensorFl [(None,)] 0 tf_op_layer_Sum_134[0][0]
tf_op_layer_Mul_404[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_944 (TensorFlo [()] 0 tf_op_layer_AddV2_269[0][0]
__________________________________________________________________________________________________
add_loss_134 (AddLoss) () 0 tf_op_layer_Mean_944[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 13s - loss: 7.6928WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0018s vs `on_train_batch_end` time: 0.1189s). Check your callbacks.
218/218 [==============================] - 1s 3ms/step - loss: 1.8960 - val_loss: 0.9709
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.8520 - val_loss: 0.7464
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6858 - val_loss: 0.6371
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6310 - val_loss: 0.6096
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5958 - val_loss: 0.5775
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5775 - val_loss: 0.5647
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5602 - val_loss: 0.5564
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5519 - val_loss: 0.5474
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5465 - val_loss: 0.5389
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5376 - val_loss: 0.5279
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5316 - val_loss: 0.5267
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5281 - val_loss: 0.5327
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5234 - val_loss: 0.5122
Epoch 14/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5185 - val_loss: 0.5144
Epoch 15/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5162 - val_loss: 0.5085
Epoch 16/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5124 - val_loss: 0.5088
Epoch 17/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5056 - val_loss: 0.5088
Epoch 18/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5049 - val_loss: 0.5002
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5008 - val_loss: 0.5025
Epoch 20/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4982 - val_loss: 0.4954
Epoch 21/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4965 - val_loss: 0.4930
Epoch 22/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4930 - val_loss: 0.4928
Epoch 23/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4927 - val_loss: 0.4890
Epoch 24/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4917 - val_loss: 0.4878
Epoch 25/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4894 - val_loss: 0.4831
Epoch 26/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4899 - val_loss: 0.4889
Epoch 27/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4869 - val_loss: 0.4822
Epoch 28/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4869 - val_loss: 0.4830
Epoch 29/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4852 - val_loss: 0.4759
Epoch 30/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4841 - val_loss: 0.4838
Epoch 31/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4820 - val_loss: 0.4856
Epoch 32/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4830 - val_loss: 0.4785
Epoch 33/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4825 - val_loss: 0.4760
Epoch 34/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4791 - val_loss: 0.4786
Epoch 35/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4804 - val_loss: 0.4776
Epoch 36/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4779 - val_loss: 0.4752
Epoch 37/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4791 - val_loss: 0.4753
Epoch 38/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4772 - val_loss: 0.4717
Epoch 39/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4754 - val_loss: 0.4752
Epoch 40/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4757 - val_loss: 0.4723
Epoch 41/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4744 - val_loss: 0.4726
Epoch 42/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4747 - val_loss: 0.4671
Epoch 43/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4735 - val_loss: 0.4690
Epoch 44/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4741 - val_loss: 0.4730
Epoch 45/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4718 - val_loss: 0.4696
Epoch 46/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4727 - val_loss: 0.4656
Epoch 47/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4716 - val_loss: 0.4691
Epoch 48/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4710 - val_loss: 0.4705
Epoch 49/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4726 - val_loss: 0.4688
Epoch 50/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4720 - val_loss: 0.4701
Epoch 51/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4703 - val_loss: 0.4691
Epoch 52/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4701 - val_loss: 0.4695
Epoch 53/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4706 - val_loss: 0.4677
Epoch 54/100
218/218 [==============================] - 1s 2ms/step - loss: 0.4693 - val_loss: 0.4678
Epoch 55/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4705 - val_loss: 0.4654
Epoch 56/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4695 - val_loss: 0.4637
Epoch 57/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4669 - val_loss: 0.4638
Epoch 58/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4674 - val_loss: 0.4631
Epoch 59/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4667 - val_loss: 0.4636
Epoch 60/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4650 - val_loss: 0.4651
Epoch 61/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4672 - val_loss: 0.4655
Epoch 62/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4667 - val_loss: 0.4668
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4666 - val_loss: 0.4586
Epoch 64/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4678 - val_loss: 0.4666
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4650 - val_loss: 0.4642
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4646 - val_loss: 0.4645
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4650 - val_loss: 0.4637
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4640 - val_loss: 0.4636
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4665 - val_loss: 0.4656
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4646 - val_loss: 0.4646
Epoch 71/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4641 - val_loss: 0.4599
Epoch 72/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4645 - val_loss: 0.4599
Epoch 73/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4664 - val_loss: 0.4592
Epoch 74/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4626 - val_loss: 0.4622
Epoch 75/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4649 - val_loss: 0.4603
Epoch 76/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4662 - val_loss: 0.4621
Epoch 77/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4634 - val_loss: 0.4584
Epoch 78/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4621 - val_loss: 0.4612
Epoch 79/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4618 - val_loss: 0.4634
Epoch 80/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4644 - val_loss: 0.4592
Epoch 81/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4634 - val_loss: 0.4597
Epoch 82/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4619 - val_loss: 0.4592
Epoch 83/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4610 - val_loss: 0.4590
Epoch 84/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4611 - val_loss: 0.4603
Epoch 85/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4624 - val_loss: 0.4613
Epoch 86/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4620 - val_loss: 0.4637
Epoch 87/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4617 - val_loss: 0.4561
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4608 - val_loss: 0.4602
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4619 - val_loss: 0.4636
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4609 - val_loss: 0.4555
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4614 - val_loss: 0.4566
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4630 - val_loss: 0.4567
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4608 - val_loss: 0.4559
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4623 - val_loss: 0.4573
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4589 - val_loss: 0.4573
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4579 - val_loss: 0.4577
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4604 - val_loss: 0.4571
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4613 - val_loss: 0.4573
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4601
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4618 - val_loss: 0.4540
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 20.40 seconds.
Calculating affinities...
Calculated affinities in 0.37 seconds.
Calculated graph and diffusion operator in 20.79 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 1.25 seconds.
Calculating KMeans...
Calculated KMeans in 25.14 seconds.
Calculated landmark operator in 29.19 seconds.
Calculating optimal t...
Automatically selected t = 30
Calculated optimal t in 3.55 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.85 seconds.
Calculating metric MDS...
Calculated metric MDS in 23.04 seconds.
Calculated PHATE in 77.45 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_407 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_408 (Dense) (None, 32) 2080 dense_407[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_408[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_408[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_409 (Dense) (None, 32) 128
_________________________________________________________________
dense_410 (Dense) (None, 64) 2112
_________________________________________________________________
dense_411 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_407 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_408 (Dense) (None, 32) 2080 dense_407[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_408[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_408[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1350 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1350 [()] 0 tf_op_layer_Shape_1350[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_135 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1350[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_135[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_405 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_135 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_405[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1351 (TensorF [(2,)] 0 tf_op_layer_Add_135[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1353 (TensorF [(2,)] 0 tf_op_layer_Add_135[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1352 (TensorF [(2,)] 0 tf_op_layer_Add_135[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1354 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1356 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1355 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1357 (TensorF [(2,)] 0 tf_op_layer_Add_135[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1359 (TensorF [(2,)] 0 tf_op_layer_Add_135[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1358 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1351 [()] 0 tf_op_layer_Shape_1351[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1353 [()] 0 tf_op_layer_Shape_1353[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1352 [()] 0 tf_op_layer_Shape_1352[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1354 [()] 0 tf_op_layer_Shape_1354[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1356 [()] 0 tf_op_layer_Shape_1356[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1355 [()] 0 tf_op_layer_Shape_1355[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1357 [()] 0 tf_op_layer_Shape_1357[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1359 [()] 0 tf_op_layer_Shape_1359[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1358 [()] 0 tf_op_layer_Shape_1358[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_810/shape ( [(3,)] 0 tf_op_layer_strided_slice_1351[0]
tf_op_layer_strided_slice_1353[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_811/shape ( [(3,)] 0 tf_op_layer_strided_slice_1352[0]
tf_op_layer_strided_slice_1353[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_812/shape ( [(3,)] 0 tf_op_layer_strided_slice_1354[0]
tf_op_layer_strided_slice_1356[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_813/shape ( [(3,)] 0 tf_op_layer_strided_slice_1355[0]
tf_op_layer_strided_slice_1356[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_814/shape ( [(3,)] 0 tf_op_layer_strided_slice_1357[0]
tf_op_layer_strided_slice_1359[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_815/shape ( [(3,)] 0 tf_op_layer_strided_slice_1358[0]
tf_op_layer_strided_slice_1359[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_810 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_135[0][0]
tf_op_layer_Reshape_810/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_810/multiples [(3,)] 0 tf_op_layer_strided_slice_1352[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_811 (Tensor [(1, None, None)] 0 tf_op_layer_Add_135[0][0]
tf_op_layer_Reshape_811/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_811/multiples [(3,)] 0 tf_op_layer_strided_slice_1351[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_812 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_812/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_812/multiples [(3,)] 0 tf_op_layer_strided_slice_1355[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_813 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_813/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_813/multiples [(3,)] 0 tf_op_layer_strided_slice_1354[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_814 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_135[0][0]
tf_op_layer_Reshape_814/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_814/multiples [(3,)] 0 tf_op_layer_strided_slice_1358[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_815 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_815/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_815/multiples [(3,)] 0 tf_op_layer_strided_slice_1357[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_810 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_810[0][0]
tf_op_layer_Tile_810/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_811 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_811[0][0]
tf_op_layer_Tile_811/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_812 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_812[0][0]
tf_op_layer_Tile_812/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_813 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_813[0][0]
tf_op_layer_Tile_813/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_814 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_814[0][0]
tf_op_layer_Tile_814/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_815 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_815[0][0]
tf_op_layer_Tile_815/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_675 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_810[0][0]
tf_op_layer_Tile_811[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_676 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_812[0][0]
tf_op_layer_Tile_813[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_677 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_814[0][0]
tf_op_layer_Tile_815[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_540 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_675[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_541 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_676[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_542 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_677[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_945 (TensorFlo [(None, None)] 0 tf_op_layer_Square_540[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_946 (TensorFlo [(None, None)] 0 tf_op_layer_Square_541[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_947 (TensorFlo [(None, None)] 0 tf_op_layer_Square_542[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_405 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_945[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_405 (TensorFlo [()] 0 tf_op_layer_strided_slice_1353[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_406 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_946[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_406 (TensorFlo [()] 0 tf_op_layer_strided_slice_1356[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_407 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_947[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_407 (TensorFlo [()] 0 tf_op_layer_strided_slice_1359[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_405 (Tensor [(None, None)] 0 tf_op_layer_Neg_405[0][0]
tf_op_layer_Cast_405[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_406 (Tensor [(None, None)] 0 tf_op_layer_Neg_406[0][0]
tf_op_layer_Cast_406[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_407 (Tensor [(None, None)] 0 tf_op_layer_Neg_407[0][0]
tf_op_layer_Cast_407[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_405 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_405[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_406 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_406[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_407 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_407[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_948 (TensorFlo [()] 0 tf_op_layer_Exp_405[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_949 (TensorFlo [()] 0 tf_op_layer_Exp_406[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_950 (TensorFlo [()] 0 tf_op_layer_Exp_407[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_679 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_270 (TensorFl [()] 0 tf_op_layer_Mean_948[0][0]
tf_op_layer_Mean_949[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_406 (TensorFlow [()] 0 tf_op_layer_Mean_950[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_543 (TensorF [(None, 97)] 0 tf_op_layer_Sub_679[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_678 (TensorFlow [()] 0 tf_op_layer_AddV2_270[0][0]
tf_op_layer_Mul_406[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_135 (TensorFlow [(None,)] 0 tf_op_layer_Square_543[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_407 (TensorFlow [()] 0 tf_op_layer_Sub_678[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_271 (TensorFl [(None,)] 0 tf_op_layer_Sum_135[0][0]
tf_op_layer_Mul_407[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_951 (TensorFlo [()] 0 tf_op_layer_AddV2_271[0][0]
__________________________________________________________________________________________________
add_loss_135 (AddLoss) () 0 tf_op_layer_Mean_951[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 7.4943WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1152s). Check your callbacks.
218/218 [==============================] - 1s 3ms/step - loss: 0.9175 - val_loss: 0.4827
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4489 - val_loss: 0.3919
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3946 - val_loss: 0.3785
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3838 - val_loss: 0.3667
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3802 - val_loss: 0.3610
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3764 - val_loss: 0.3627
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3739 - val_loss: 0.3575
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3712 - val_loss: 0.3597
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3705 - val_loss: 0.3570
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3679 - val_loss: 0.3528
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3653 - val_loss: 0.3537
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3655 - val_loss: 0.3544
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3647 - val_loss: 0.3515
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3619 - val_loss: 0.3492
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3616 - val_loss: 0.3491
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3616 - val_loss: 0.3465
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3589 - val_loss: 0.3472
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3602 - val_loss: 0.3454
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3578 - val_loss: 0.3457
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3582 - val_loss: 0.3430
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3554 - val_loss: 0.3444
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3561 - val_loss: 0.3417
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3548 - val_loss: 0.3422
Epoch 24/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3540 - val_loss: 0.3423
Epoch 25/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3530 - val_loss: 0.3404
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3533 - val_loss: 0.3402
Epoch 27/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3511 - val_loss: 0.3416
Epoch 28/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3521 - val_loss: 0.3405
Epoch 29/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3518 - val_loss: 0.3422
Epoch 30/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3517 - val_loss: 0.3407
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3505 - val_loss: 0.3388
Epoch 32/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3510 - val_loss: 0.3377
Epoch 33/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3499 - val_loss: 0.3371
Epoch 34/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3492 - val_loss: 0.3377
Epoch 35/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3483 - val_loss: 0.3395
Epoch 36/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3487 - val_loss: 0.3341
Epoch 37/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3502 - val_loss: 0.3383
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3482 - val_loss: 0.3359
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3373
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3338
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3495 - val_loss: 0.3360
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3364
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3363
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3361
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3377
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3473 - val_loss: 0.3340
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3372
Epoch 48/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3468 - val_loss: 0.3369
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3481 - val_loss: 0.3367
Epoch 50/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3472 - val_loss: 0.3339
Epoch 51/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3457 - val_loss: 0.3335
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3476 - val_loss: 0.3360
Epoch 53/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3448 - val_loss: 0.3355
Epoch 54/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3467 - val_loss: 0.3354
Epoch 55/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3459 - val_loss: 0.3316
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3341
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3329
Epoch 58/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3456 - val_loss: 0.3357
Epoch 59/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3460 - val_loss: 0.3344
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3333
Epoch 61/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3463 - val_loss: 0.3320
Epoch 62/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3458 - val_loss: 0.3368
Epoch 63/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3451 - val_loss: 0.3334
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3327
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3454 - val_loss: 0.3326
Epoch 66/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3454 - val_loss: 0.3336
Epoch 67/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3447 - val_loss: 0.3342
Epoch 68/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3439 - val_loss: 0.3317
Epoch 69/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3451 - val_loss: 0.3321
Epoch 70/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3436 - val_loss: 0.3311
Epoch 71/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3442 - val_loss: 0.3363
Epoch 72/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3446 - val_loss: 0.3360
Epoch 73/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3447 - val_loss: 0.3300
Epoch 74/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3432 - val_loss: 0.3342
Epoch 75/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3442 - val_loss: 0.3353
Epoch 76/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3430 - val_loss: 0.3321
Epoch 77/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3431 - val_loss: 0.3337
Epoch 78/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3435 - val_loss: 0.3337
Epoch 79/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3438 - val_loss: 0.3318
Epoch 80/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3437 - val_loss: 0.3324
Epoch 81/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3434 - val_loss: 0.3316
Epoch 82/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3444 - val_loss: 0.3317
Epoch 83/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3440 - val_loss: 0.3316
Epoch 84/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3445 - val_loss: 0.3313
Epoch 85/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3423 - val_loss: 0.3338
Epoch 86/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3440 - val_loss: 0.3338
Epoch 87/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3437 - val_loss: 0.3314
Epoch 88/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3415 - val_loss: 0.3318
Epoch 89/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3439 - val_loss: 0.3311
Epoch 90/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3440 - val_loss: 0.3327
Epoch 91/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3429 - val_loss: 0.3309
Epoch 92/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3427 - val_loss: 0.3324
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3425 - val_loss: 0.3335
Epoch 94/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3439 - val_loss: 0.3316
Epoch 95/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3426 - val_loss: 0.3324
Epoch 96/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3420 - val_loss: 0.3303
Epoch 97/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3422 - val_loss: 0.3315
Epoch 98/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3429 - val_loss: 0.3354
Epoch 99/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3422 - val_loss: 0.3308
Epoch 100/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3423 - val_loss: 0.3319
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_412 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1360 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1360 [()] 0 tf_op_layer_Shape_1360[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_136 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1360[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_136[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_408 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_136 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_408[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1361 (TensorF [(2,)] 0 tf_op_layer_Add_136[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1363 (TensorF [(2,)] 0 tf_op_layer_Add_136[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1362 (TensorF [(2,)] 0 tf_op_layer_Add_136[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1364 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1366 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1365 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1367 (TensorF [(2,)] 0 tf_op_layer_Add_136[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1369 (TensorF [(2,)] 0 tf_op_layer_Add_136[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1368 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1361 [()] 0 tf_op_layer_Shape_1361[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1363 [()] 0 tf_op_layer_Shape_1363[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1362 [()] 0 tf_op_layer_Shape_1362[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1364 [()] 0 tf_op_layer_Shape_1364[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1366 [()] 0 tf_op_layer_Shape_1366[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1365 [()] 0 tf_op_layer_Shape_1365[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1367 [()] 0 tf_op_layer_Shape_1367[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1369 [()] 0 tf_op_layer_Shape_1369[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1368 [()] 0 tf_op_layer_Shape_1368[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_816/shape ( [(3,)] 0 tf_op_layer_strided_slice_1361[0]
tf_op_layer_strided_slice_1363[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_817/shape ( [(3,)] 0 tf_op_layer_strided_slice_1362[0]
tf_op_layer_strided_slice_1363[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_818/shape ( [(3,)] 0 tf_op_layer_strided_slice_1364[0]
tf_op_layer_strided_slice_1366[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_819/shape ( [(3,)] 0 tf_op_layer_strided_slice_1365[0]
tf_op_layer_strided_slice_1366[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_820/shape ( [(3,)] 0 tf_op_layer_strided_slice_1367[0]
tf_op_layer_strided_slice_1369[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_821/shape ( [(3,)] 0 tf_op_layer_strided_slice_1368[0]
tf_op_layer_strided_slice_1369[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_816 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_136[0][0]
tf_op_layer_Reshape_816/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_816/multiples [(3,)] 0 tf_op_layer_strided_slice_1362[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_817 (Tensor [(1, None, None)] 0 tf_op_layer_Add_136[0][0]
tf_op_layer_Reshape_817/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_817/multiples [(3,)] 0 tf_op_layer_strided_slice_1361[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_818 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_818/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_818/multiples [(3,)] 0 tf_op_layer_strided_slice_1365[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_819 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_819/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_819/multiples [(3,)] 0 tf_op_layer_strided_slice_1364[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_820 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_136[0][0]
tf_op_layer_Reshape_820/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_820/multiples [(3,)] 0 tf_op_layer_strided_slice_1368[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_821 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_821/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_821/multiples [(3,)] 0 tf_op_layer_strided_slice_1367[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_816 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_816[0][0]
tf_op_layer_Tile_816/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_817 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_817[0][0]
tf_op_layer_Tile_817/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_818 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_818[0][0]
tf_op_layer_Tile_818/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_819 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_819[0][0]
tf_op_layer_Tile_819/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_820 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_820[0][0]
tf_op_layer_Tile_820/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_821 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_821[0][0]
tf_op_layer_Tile_821/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_680 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_816[0][0]
tf_op_layer_Tile_817[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_681 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_818[0][0]
tf_op_layer_Tile_819[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_682 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_820[0][0]
tf_op_layer_Tile_821[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_544 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_680[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_545 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_681[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_546 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_682[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_952 (TensorFlo [(None, None)] 0 tf_op_layer_Square_544[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_953 (TensorFlo [(None, None)] 0 tf_op_layer_Square_545[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_954 (TensorFlo [(None, None)] 0 tf_op_layer_Square_546[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_408 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_952[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_408 (TensorFlo [()] 0 tf_op_layer_strided_slice_1363[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_409 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_953[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_409 (TensorFlo [()] 0 tf_op_layer_strided_slice_1366[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_410 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_954[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_410 (TensorFlo [()] 0 tf_op_layer_strided_slice_1369[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_408 (Tensor [(None, None)] 0 tf_op_layer_Neg_408[0][0]
tf_op_layer_Cast_408[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_409 (Tensor [(None, None)] 0 tf_op_layer_Neg_409[0][0]
tf_op_layer_Cast_409[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_410 (Tensor [(None, None)] 0 tf_op_layer_Neg_410[0][0]
tf_op_layer_Cast_410[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_408 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_408[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_409 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_409[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_410 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_410[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_955 (TensorFlo [()] 0 tf_op_layer_Exp_408[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_956 (TensorFlo [()] 0 tf_op_layer_Exp_409[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_957 (TensorFlo [()] 0 tf_op_layer_Exp_410[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_684 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_272 (TensorFl [()] 0 tf_op_layer_Mean_955[0][0]
tf_op_layer_Mean_956[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_409 (TensorFlow [()] 0 tf_op_layer_Mean_957[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_547 (TensorF [(None, 97)] 0 tf_op_layer_Sub_684[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_683 (TensorFlow [()] 0 tf_op_layer_AddV2_272[0][0]
tf_op_layer_Mul_409[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_136 (TensorFlow [(None,)] 0 tf_op_layer_Square_547[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_410 (TensorFlow [()] 0 tf_op_layer_Sub_683[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_273 (TensorFl [(None,)] 0 tf_op_layer_Sum_136[0][0]
tf_op_layer_Mul_410[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_958 (TensorFlo [()] 0 tf_op_layer_AddV2_273[0][0]
__________________________________________________________________________________________________
add_loss_136 (AddLoss) () 0 tf_op_layer_Mean_958[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 16s - loss: 8.2004WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1537s). Check your callbacks.
218/218 [==============================] - 1s 3ms/step - loss: 2.0031 - val_loss: 0.9518
Epoch 2/100
218/218 [==============================] - 0s 2ms/step - loss: 0.8274 - val_loss: 0.7353
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6936 - val_loss: 0.6573
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6334 - val_loss: 0.6192
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.6005 - val_loss: 0.6010
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5824 - val_loss: 0.5831
Epoch 7/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5656 - val_loss: 0.5623
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5575 - val_loss: 0.5534
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5465 - val_loss: 0.5498
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5380 - val_loss: 0.5360
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5319 - val_loss: 0.5377
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5243 - val_loss: 0.5269
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5226 - val_loss: 0.5225
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5157 - val_loss: 0.5255
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5143 - val_loss: 0.5178
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5092 - val_loss: 0.5163
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5077 - val_loss: 0.5042
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5032 - val_loss: 0.5052
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5001 - val_loss: 0.5065
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4989 - val_loss: 0.5063
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4963 - val_loss: 0.4975
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4964 - val_loss: 0.5011
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4935 - val_loss: 0.4993
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4931 - val_loss: 0.4959
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4922 - val_loss: 0.4910
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4872 - val_loss: 0.4909
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4883 - val_loss: 0.4965
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4851 - val_loss: 0.4906
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4850 - val_loss: 0.4946
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4834 - val_loss: 0.4876
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4828 - val_loss: 0.4892
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4811 - val_loss: 0.4917
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4796 - val_loss: 0.4871
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4825 - val_loss: 0.4860
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4786 - val_loss: 0.4823
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4793 - val_loss: 0.4814
Epoch 37/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4771 - val_loss: 0.4865
Epoch 38/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4774 - val_loss: 0.4875
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4767 - val_loss: 0.4830
Epoch 40/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4779 - val_loss: 0.4841
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4754 - val_loss: 0.4773
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4760 - val_loss: 0.4787
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4742 - val_loss: 0.4742
Epoch 44/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4738 - val_loss: 0.4791
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4737 - val_loss: 0.4793
Epoch 46/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4754 - val_loss: 0.4783
Epoch 47/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4738 - val_loss: 0.4800
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4727 - val_loss: 0.4786
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4730 - val_loss: 0.4793
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4716 - val_loss: 0.4763
Epoch 51/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4716 - val_loss: 0.4783
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4695 - val_loss: 0.4752
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4678 - val_loss: 0.4762
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4697 - val_loss: 0.4750
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4686 - val_loss: 0.4742
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4675 - val_loss: 0.4731
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4676 - val_loss: 0.4716
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4677 - val_loss: 0.4730
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4705 - val_loss: 0.4719
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4665 - val_loss: 0.4751
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4683 - val_loss: 0.4728
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4687 - val_loss: 0.4738
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4664 - val_loss: 0.4730
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4675 - val_loss: 0.4724
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4654 - val_loss: 0.4701
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4650 - val_loss: 0.4702
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4673 - val_loss: 0.4715
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4654 - val_loss: 0.4698
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4642 - val_loss: 0.4744
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4654 - val_loss: 0.4687
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4633 - val_loss: 0.4722
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4644 - val_loss: 0.4727
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4656 - val_loss: 0.4724
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4635 - val_loss: 0.4722
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4628 - val_loss: 0.4681
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4629 - val_loss: 0.4693
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4621 - val_loss: 0.4651
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4638 - val_loss: 0.4704
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4628 - val_loss: 0.4705
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4616 - val_loss: 0.4707
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4644 - val_loss: 0.4689
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4614 - val_loss: 0.4676
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4630 - val_loss: 0.4675
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4622 - val_loss: 0.4657
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4610 - val_loss: 0.4688
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4656
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4604 - val_loss: 0.4672
Epoch 88/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4611 - val_loss: 0.4654
Epoch 89/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4619 - val_loss: 0.4655
Epoch 90/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4607 - val_loss: 0.4676
Epoch 91/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4604 - val_loss: 0.4663
Epoch 92/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4604 - val_loss: 0.4660
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4591 - val_loss: 0.4679
Epoch 94/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4606 - val_loss: 0.4633
Epoch 95/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4620 - val_loss: 0.4684
Epoch 96/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4601 - val_loss: 0.4651
Epoch 97/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4606 - val_loss: 0.4663
Epoch 98/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4603 - val_loss: 0.4644
Epoch 99/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4592 - val_loss: 0.4643
Epoch 100/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4581 - val_loss: 0.4663
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 21.07 seconds.
Calculating affinities...
Calculated affinities in 0.32 seconds.
Calculated graph and diffusion operator in 21.40 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 1.05 seconds.
Calculating KMeans...
Calculated KMeans in 26.50 seconds.
Calculated landmark operator in 29.58 seconds.
Calculating optimal t...
Automatically selected t = 47
Calculated optimal t in 3.69 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 1.09 seconds.
Calculating metric MDS...
Calculated metric MDS in 23.91 seconds.
Calculated PHATE in 79.69 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_413 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_414 (Dense) (None, 32) 2080 dense_413[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_414[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_414[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_415 (Dense) (None, 32) 128
_________________________________________________________________
dense_416 (Dense) (None, 64) 2112
_________________________________________________________________
dense_417 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_413 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_414 (Dense) (None, 32) 2080 dense_413[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_414[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_414[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1370 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1370 [()] 0 tf_op_layer_Shape_1370[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_137 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1370[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_137[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_411 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_137 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_411[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1371 (TensorF [(2,)] 0 tf_op_layer_Add_137[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1373 (TensorF [(2,)] 0 tf_op_layer_Add_137[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1372 (TensorF [(2,)] 0 tf_op_layer_Add_137[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1374 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1376 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1375 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1377 (TensorF [(2,)] 0 tf_op_layer_Add_137[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1379 (TensorF [(2,)] 0 tf_op_layer_Add_137[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1378 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1371 [()] 0 tf_op_layer_Shape_1371[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1373 [()] 0 tf_op_layer_Shape_1373[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1372 [()] 0 tf_op_layer_Shape_1372[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1374 [()] 0 tf_op_layer_Shape_1374[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1376 [()] 0 tf_op_layer_Shape_1376[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1375 [()] 0 tf_op_layer_Shape_1375[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1377 [()] 0 tf_op_layer_Shape_1377[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1379 [()] 0 tf_op_layer_Shape_1379[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1378 [()] 0 tf_op_layer_Shape_1378[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_822/shape ( [(3,)] 0 tf_op_layer_strided_slice_1371[0]
tf_op_layer_strided_slice_1373[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_823/shape ( [(3,)] 0 tf_op_layer_strided_slice_1372[0]
tf_op_layer_strided_slice_1373[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_824/shape ( [(3,)] 0 tf_op_layer_strided_slice_1374[0]
tf_op_layer_strided_slice_1376[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_825/shape ( [(3,)] 0 tf_op_layer_strided_slice_1375[0]
tf_op_layer_strided_slice_1376[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_826/shape ( [(3,)] 0 tf_op_layer_strided_slice_1377[0]
tf_op_layer_strided_slice_1379[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_827/shape ( [(3,)] 0 tf_op_layer_strided_slice_1378[0]
tf_op_layer_strided_slice_1379[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_822 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_137[0][0]
tf_op_layer_Reshape_822/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_822/multiples [(3,)] 0 tf_op_layer_strided_slice_1372[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_823 (Tensor [(1, None, None)] 0 tf_op_layer_Add_137[0][0]
tf_op_layer_Reshape_823/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_823/multiples [(3,)] 0 tf_op_layer_strided_slice_1371[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_824 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_824/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_824/multiples [(3,)] 0 tf_op_layer_strided_slice_1375[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_825 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_825/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_825/multiples [(3,)] 0 tf_op_layer_strided_slice_1374[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_826 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_137[0][0]
tf_op_layer_Reshape_826/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_826/multiples [(3,)] 0 tf_op_layer_strided_slice_1378[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_827 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_827/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_827/multiples [(3,)] 0 tf_op_layer_strided_slice_1377[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_822 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_822[0][0]
tf_op_layer_Tile_822/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_823 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_823[0][0]
tf_op_layer_Tile_823/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_824 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_824[0][0]
tf_op_layer_Tile_824/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_825 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_825[0][0]
tf_op_layer_Tile_825/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_826 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_826[0][0]
tf_op_layer_Tile_826/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_827 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_827[0][0]
tf_op_layer_Tile_827/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_685 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_822[0][0]
tf_op_layer_Tile_823[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_686 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_824[0][0]
tf_op_layer_Tile_825[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_687 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_826[0][0]
tf_op_layer_Tile_827[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_548 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_685[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_549 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_686[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_550 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_687[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_959 (TensorFlo [(None, None)] 0 tf_op_layer_Square_548[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_960 (TensorFlo [(None, None)] 0 tf_op_layer_Square_549[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_961 (TensorFlo [(None, None)] 0 tf_op_layer_Square_550[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_411 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_959[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_411 (TensorFlo [()] 0 tf_op_layer_strided_slice_1373[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_412 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_960[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_412 (TensorFlo [()] 0 tf_op_layer_strided_slice_1376[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_413 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_961[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_413 (TensorFlo [()] 0 tf_op_layer_strided_slice_1379[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_411 (Tensor [(None, None)] 0 tf_op_layer_Neg_411[0][0]
tf_op_layer_Cast_411[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_412 (Tensor [(None, None)] 0 tf_op_layer_Neg_412[0][0]
tf_op_layer_Cast_412[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_413 (Tensor [(None, None)] 0 tf_op_layer_Neg_413[0][0]
tf_op_layer_Cast_413[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_411 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_411[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_412 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_412[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_413 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_413[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_962 (TensorFlo [()] 0 tf_op_layer_Exp_411[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_963 (TensorFlo [()] 0 tf_op_layer_Exp_412[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_964 (TensorFlo [()] 0 tf_op_layer_Exp_413[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_689 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_274 (TensorFl [()] 0 tf_op_layer_Mean_962[0][0]
tf_op_layer_Mean_963[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_412 (TensorFlow [()] 0 tf_op_layer_Mean_964[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_551 (TensorF [(None, 97)] 0 tf_op_layer_Sub_689[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_688 (TensorFlow [()] 0 tf_op_layer_AddV2_274[0][0]
tf_op_layer_Mul_412[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_137 (TensorFlow [(None,)] 0 tf_op_layer_Square_551[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_413 (TensorFlow [()] 0 tf_op_layer_Sub_688[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_275 (TensorFl [(None,)] 0 tf_op_layer_Sum_137[0][0]
tf_op_layer_Mul_413[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_965 (TensorFlo [()] 0 tf_op_layer_AddV2_275[0][0]
__________________________________________________________________________________________________
add_loss_137 (AddLoss) () 0 tf_op_layer_Mean_965[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 20s - loss: 7.9611WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.1913s). Check your callbacks.
218/218 [==============================] - 1s 4ms/step - loss: 0.8790 - val_loss: 0.4189
Epoch 2/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4016 - val_loss: 0.3904
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3833 - val_loss: 0.3802
Epoch 4/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3762 - val_loss: 0.3777
Epoch 5/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3708 - val_loss: 0.3716
Epoch 6/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3666 - val_loss: 0.3745
Epoch 7/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3662 - val_loss: 0.3732
Epoch 8/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3624 - val_loss: 0.3646
Epoch 9/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3596 - val_loss: 0.3623
Epoch 10/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3598 - val_loss: 0.3622
Epoch 11/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3587 - val_loss: 0.3636
Epoch 12/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3585 - val_loss: 0.3613
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3555 - val_loss: 0.3593
Epoch 14/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3549 - val_loss: 0.3590
Epoch 15/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3551 - val_loss: 0.3572
Epoch 16/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3536 - val_loss: 0.3568
Epoch 17/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3528 - val_loss: 0.3560
Epoch 18/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3543 - val_loss: 0.3612
Epoch 19/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3535 - val_loss: 0.3576
Epoch 20/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3513 - val_loss: 0.3556
Epoch 21/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3514 - val_loss: 0.3556
Epoch 22/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3510 - val_loss: 0.3525
Epoch 23/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3511 - val_loss: 0.3561
Epoch 24/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3499 - val_loss: 0.3556
Epoch 25/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3495 - val_loss: 0.3542
Epoch 26/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3504 - val_loss: 0.3538
Epoch 27/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3494 - val_loss: 0.3548
Epoch 28/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3482 - val_loss: 0.3524
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3490 - val_loss: 0.3528
Epoch 30/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3491 - val_loss: 0.3546
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3485 - val_loss: 0.3536
Epoch 32/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3484 - val_loss: 0.3522
Epoch 33/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3481 - val_loss: 0.3544
Epoch 34/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3482 - val_loss: 0.3513
Epoch 35/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3465 - val_loss: 0.3502
Epoch 36/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3478 - val_loss: 0.3538
Epoch 37/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3468 - val_loss: 0.3521
Epoch 38/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3466 - val_loss: 0.3560
Epoch 39/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3465 - val_loss: 0.3495
Epoch 40/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3467 - val_loss: 0.3505
Epoch 41/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3469 - val_loss: 0.3503
Epoch 42/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3464 - val_loss: 0.3516
Epoch 43/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3469 - val_loss: 0.3524
Epoch 44/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3465 - val_loss: 0.3514
Epoch 45/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3458 - val_loss: 0.3532
Epoch 46/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3460 - val_loss: 0.3496
Epoch 47/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3463 - val_loss: 0.3534
Epoch 48/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3450 - val_loss: 0.3512
Epoch 49/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3453 - val_loss: 0.3507
Epoch 50/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3470 - val_loss: 0.3524
Epoch 51/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3455 - val_loss: 0.3485
Epoch 52/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3458 - val_loss: 0.3489
Epoch 53/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3461 - val_loss: 0.3487
Epoch 54/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3431 - val_loss: 0.3511
Epoch 55/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3447 - val_loss: 0.3497
Epoch 56/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3454 - val_loss: 0.3490
Epoch 57/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3458 - val_loss: 0.3508
Epoch 58/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3438 - val_loss: 0.3492
Epoch 59/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3427 - val_loss: 0.3484
Epoch 60/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3443 - val_loss: 0.3481
Epoch 61/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3438 - val_loss: 0.3498
Epoch 62/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3447 - val_loss: 0.3484
Epoch 63/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3439 - val_loss: 0.3493
Epoch 64/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3441 - val_loss: 0.3487
Epoch 65/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3432 - val_loss: 0.3528
Epoch 66/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3442 - val_loss: 0.3492
Epoch 67/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3437 - val_loss: 0.3481
Epoch 68/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3452 - val_loss: 0.3495
Epoch 69/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3437 - val_loss: 0.3479
Epoch 70/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3424 - val_loss: 0.3505
Epoch 71/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3433 - val_loss: 0.3480
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3520
Epoch 73/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3442 - val_loss: 0.3506
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3477
Epoch 75/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3435 - val_loss: 0.3494
Epoch 76/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3432 - val_loss: 0.3478
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3471
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3488
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3490
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3511
Epoch 81/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3426 - val_loss: 0.3470
Epoch 82/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3410 - val_loss: 0.3484
Epoch 83/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3416 - val_loss: 0.3496
Epoch 84/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3429 - val_loss: 0.3470
Epoch 85/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3425 - val_loss: 0.3482
Epoch 86/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3419 - val_loss: 0.3509
Epoch 87/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3427 - val_loss: 0.3467
Epoch 88/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3423 - val_loss: 0.3494
Epoch 89/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3409 - val_loss: 0.3472
Epoch 90/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3427 - val_loss: 0.3505
Epoch 91/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3416 - val_loss: 0.3464
Epoch 92/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3420 - val_loss: 0.3469
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3420 - val_loss: 0.3474
Epoch 94/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3429 - val_loss: 0.3501
Epoch 95/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3418 - val_loss: 0.3488
Epoch 96/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3421 - val_loss: 0.3479
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3481
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3460
Epoch 99/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3412 - val_loss: 0.3503
Epoch 100/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3416 - val_loss: 0.3456
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_418 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1380 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1380 [()] 0 tf_op_layer_Shape_1380[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_138 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1380[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_138[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_414 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_138 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_414[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1381 (TensorF [(2,)] 0 tf_op_layer_Add_138[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1383 (TensorF [(2,)] 0 tf_op_layer_Add_138[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1382 (TensorF [(2,)] 0 tf_op_layer_Add_138[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1384 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1386 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1385 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1387 (TensorF [(2,)] 0 tf_op_layer_Add_138[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1389 (TensorF [(2,)] 0 tf_op_layer_Add_138[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1388 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1381 [()] 0 tf_op_layer_Shape_1381[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1383 [()] 0 tf_op_layer_Shape_1383[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1382 [()] 0 tf_op_layer_Shape_1382[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1384 [()] 0 tf_op_layer_Shape_1384[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1386 [()] 0 tf_op_layer_Shape_1386[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1385 [()] 0 tf_op_layer_Shape_1385[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1387 [()] 0 tf_op_layer_Shape_1387[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1389 [()] 0 tf_op_layer_Shape_1389[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1388 [()] 0 tf_op_layer_Shape_1388[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_828/shape ( [(3,)] 0 tf_op_layer_strided_slice_1381[0]
tf_op_layer_strided_slice_1383[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_829/shape ( [(3,)] 0 tf_op_layer_strided_slice_1382[0]
tf_op_layer_strided_slice_1383[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_830/shape ( [(3,)] 0 tf_op_layer_strided_slice_1384[0]
tf_op_layer_strided_slice_1386[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_831/shape ( [(3,)] 0 tf_op_layer_strided_slice_1385[0]
tf_op_layer_strided_slice_1386[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_832/shape ( [(3,)] 0 tf_op_layer_strided_slice_1387[0]
tf_op_layer_strided_slice_1389[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_833/shape ( [(3,)] 0 tf_op_layer_strided_slice_1388[0]
tf_op_layer_strided_slice_1389[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_828 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_138[0][0]
tf_op_layer_Reshape_828/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_828/multiples [(3,)] 0 tf_op_layer_strided_slice_1382[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_829 (Tensor [(1, None, None)] 0 tf_op_layer_Add_138[0][0]
tf_op_layer_Reshape_829/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_829/multiples [(3,)] 0 tf_op_layer_strided_slice_1381[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_830 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_830/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_830/multiples [(3,)] 0 tf_op_layer_strided_slice_1385[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_831 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_831/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_831/multiples [(3,)] 0 tf_op_layer_strided_slice_1384[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_832 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_138[0][0]
tf_op_layer_Reshape_832/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_832/multiples [(3,)] 0 tf_op_layer_strided_slice_1388[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_833 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_833/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_833/multiples [(3,)] 0 tf_op_layer_strided_slice_1387[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_828 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_828[0][0]
tf_op_layer_Tile_828/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_829 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_829[0][0]
tf_op_layer_Tile_829/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_830 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_830[0][0]
tf_op_layer_Tile_830/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_831 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_831[0][0]
tf_op_layer_Tile_831/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_832 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_832[0][0]
tf_op_layer_Tile_832/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_833 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_833[0][0]
tf_op_layer_Tile_833/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_690 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_828[0][0]
tf_op_layer_Tile_829[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_691 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_830[0][0]
tf_op_layer_Tile_831[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_692 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_832[0][0]
tf_op_layer_Tile_833[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_552 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_690[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_553 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_691[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_554 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_692[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_966 (TensorFlo [(None, None)] 0 tf_op_layer_Square_552[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_967 (TensorFlo [(None, None)] 0 tf_op_layer_Square_553[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_968 (TensorFlo [(None, None)] 0 tf_op_layer_Square_554[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_414 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_966[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_414 (TensorFlo [()] 0 tf_op_layer_strided_slice_1383[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_415 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_967[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_415 (TensorFlo [()] 0 tf_op_layer_strided_slice_1386[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_416 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_968[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_416 (TensorFlo [()] 0 tf_op_layer_strided_slice_1389[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_414 (Tensor [(None, None)] 0 tf_op_layer_Neg_414[0][0]
tf_op_layer_Cast_414[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_415 (Tensor [(None, None)] 0 tf_op_layer_Neg_415[0][0]
tf_op_layer_Cast_415[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_416 (Tensor [(None, None)] 0 tf_op_layer_Neg_416[0][0]
tf_op_layer_Cast_416[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_414 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_414[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_415 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_415[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_416 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_416[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_969 (TensorFlo [()] 0 tf_op_layer_Exp_414[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_970 (TensorFlo [()] 0 tf_op_layer_Exp_415[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_971 (TensorFlo [()] 0 tf_op_layer_Exp_416[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_694 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_276 (TensorFl [()] 0 tf_op_layer_Mean_969[0][0]
tf_op_layer_Mean_970[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_415 (TensorFlow [()] 0 tf_op_layer_Mean_971[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_555 (TensorF [(None, 97)] 0 tf_op_layer_Sub_694[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_693 (TensorFlow [()] 0 tf_op_layer_AddV2_276[0][0]
tf_op_layer_Mul_415[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_138 (TensorFlow [(None,)] 0 tf_op_layer_Square_555[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_416 (TensorFlow [()] 0 tf_op_layer_Sub_693[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_277 (TensorFl [(None,)] 0 tf_op_layer_Sum_138[0][0]
tf_op_layer_Mul_416[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_972 (TensorFlo [()] 0 tf_op_layer_AddV2_277[0][0]
__________________________________________________________________________________________________
add_loss_138 (AddLoss) () 0 tf_op_layer_Mean_972[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 19s - loss: 7.9433WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0015s vs `on_train_batch_end` time: 0.1756s). Check your callbacks.
218/218 [==============================] - 1s 4ms/step - loss: 1.9944 - val_loss: 0.9274
Epoch 2/100
218/218 [==============================] - 0s 2ms/step - loss: 0.8305 - val_loss: 0.7443
Epoch 3/100
218/218 [==============================] - 0s 2ms/step - loss: 0.7072 - val_loss: 0.6473
Epoch 4/100
218/218 [==============================] - 0s 2ms/step - loss: 0.6454 - val_loss: 0.5985
Epoch 5/100
218/218 [==============================] - 0s 2ms/step - loss: 0.6084 - val_loss: 0.5818
Epoch 6/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5876 - val_loss: 0.5662
Epoch 7/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5730 - val_loss: 0.5462
Epoch 8/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5610 - val_loss: 0.5348
Epoch 9/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5519 - val_loss: 0.5322
Epoch 10/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5434 - val_loss: 0.5203
Epoch 11/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5342 - val_loss: 0.5245
Epoch 12/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5299 - val_loss: 0.5202
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5232 - val_loss: 0.5088
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5207 - val_loss: 0.5030
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5164 - val_loss: 0.5007
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5158 - val_loss: 0.5020
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5111 - val_loss: 0.5013
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5106 - val_loss: 0.4903
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5085 - val_loss: 0.4906
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5030 - val_loss: 0.4895
Epoch 21/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5043 - val_loss: 0.4867
Epoch 22/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5004 - val_loss: 0.4884
Epoch 23/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5004 - val_loss: 0.4914
Epoch 24/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4965 - val_loss: 0.4853
Epoch 25/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4958 - val_loss: 0.4813
Epoch 26/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4922 - val_loss: 0.4766
Epoch 27/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4956 - val_loss: 0.4762
Epoch 28/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4914 - val_loss: 0.4767
Epoch 29/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4925 - val_loss: 0.4776
Epoch 30/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4899 - val_loss: 0.4774
Epoch 31/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4880 - val_loss: 0.4822
Epoch 32/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4867 - val_loss: 0.4723
Epoch 33/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4868 - val_loss: 0.4711
Epoch 34/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4843 - val_loss: 0.4641
Epoch 35/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4854 - val_loss: 0.4715
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4859 - val_loss: 0.4744
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4846 - val_loss: 0.4710
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4820 - val_loss: 0.4671
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4801 - val_loss: 0.4689
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4828 - val_loss: 0.4644
Epoch 41/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4810 - val_loss: 0.4650
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4798 - val_loss: 0.4635
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4801 - val_loss: 0.4645
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4783 - val_loss: 0.4594
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4791 - val_loss: 0.4634
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4788 - val_loss: 0.4669
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4779 - val_loss: 0.4645
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4789 - val_loss: 0.4634
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4769 - val_loss: 0.4582
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4772 - val_loss: 0.4610
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4770 - val_loss: 0.4628
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4778 - val_loss: 0.4582
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4768 - val_loss: 0.4623
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4772 - val_loss: 0.4608
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4760 - val_loss: 0.4611
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4756 - val_loss: 0.4600
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4760 - val_loss: 0.4591
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4758 - val_loss: 0.4583
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4745 - val_loss: 0.4640
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4715 - val_loss: 0.4615
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4740 - val_loss: 0.4584
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4737 - val_loss: 0.4565
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4728 - val_loss: 0.4557
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4703 - val_loss: 0.4547
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4714 - val_loss: 0.4558
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4702 - val_loss: 0.4613
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4721 - val_loss: 0.4565
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4701 - val_loss: 0.4542
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4708 - val_loss: 0.4618
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4698 - val_loss: 0.4545
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4687 - val_loss: 0.4561
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4711 - val_loss: 0.4591
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4704 - val_loss: 0.4589
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4709 - val_loss: 0.4525
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4718 - val_loss: 0.4555
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4683 - val_loss: 0.4560
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4696 - val_loss: 0.4537
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4698 - val_loss: 0.4560
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4691 - val_loss: 0.4546
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4687 - val_loss: 0.4535
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4668 - val_loss: 0.4524
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4694 - val_loss: 0.4551
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4693 - val_loss: 0.4525
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4707 - val_loss: 0.4531
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4657 - val_loss: 0.4529
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4666 - val_loss: 0.4509
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4696 - val_loss: 0.4535
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4675 - val_loss: 0.4567
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4682 - val_loss: 0.4530
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4676 - val_loss: 0.4535
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4673 - val_loss: 0.4554
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4678 - val_loss: 0.4536
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4672 - val_loss: 0.4520
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4667 - val_loss: 0.4497
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4673 - val_loss: 0.4497
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4684 - val_loss: 0.4528
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4677 - val_loss: 0.4520
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4657 - val_loss: 0.4525
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4643 - val_loss: 0.4524
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4652 - val_loss: 0.4499
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 26.02 seconds.
Calculating affinities...
Calculated affinities in 0.33 seconds.
Calculated graph and diffusion operator in 26.37 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 1.43 seconds.
Calculating KMeans...
Calculated KMeans in 26.66 seconds.
Calculated landmark operator in 31.03 seconds.
Calculating optimal t...
Automatically selected t = 45
Calculated optimal t in 7.60 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 1.02 seconds.
Calculating metric MDS...
Calculated metric MDS in 23.77 seconds.
Calculated PHATE in 89.83 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_419 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_420 (Dense) (None, 32) 2080 dense_419[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_420[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_420[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_421 (Dense) (None, 32) 128
_________________________________________________________________
dense_422 (Dense) (None, 64) 2112
_________________________________________________________________
dense_423 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_419 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_420 (Dense) (None, 32) 2080 dense_419[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_420[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_420[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1390 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1390 [()] 0 tf_op_layer_Shape_1390[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_139 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1390[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_139[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_417 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_139 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_417[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1391 (TensorF [(2,)] 0 tf_op_layer_Add_139[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1393 (TensorF [(2,)] 0 tf_op_layer_Add_139[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1392 (TensorF [(2,)] 0 tf_op_layer_Add_139[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1394 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1396 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1395 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1397 (TensorF [(2,)] 0 tf_op_layer_Add_139[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1399 (TensorF [(2,)] 0 tf_op_layer_Add_139[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1398 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1391 [()] 0 tf_op_layer_Shape_1391[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1393 [()] 0 tf_op_layer_Shape_1393[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1392 [()] 0 tf_op_layer_Shape_1392[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1394 [()] 0 tf_op_layer_Shape_1394[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1396 [()] 0 tf_op_layer_Shape_1396[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1395 [()] 0 tf_op_layer_Shape_1395[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1397 [()] 0 tf_op_layer_Shape_1397[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1399 [()] 0 tf_op_layer_Shape_1399[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1398 [()] 0 tf_op_layer_Shape_1398[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_834/shape ( [(3,)] 0 tf_op_layer_strided_slice_1391[0]
tf_op_layer_strided_slice_1393[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_835/shape ( [(3,)] 0 tf_op_layer_strided_slice_1392[0]
tf_op_layer_strided_slice_1393[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_836/shape ( [(3,)] 0 tf_op_layer_strided_slice_1394[0]
tf_op_layer_strided_slice_1396[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_837/shape ( [(3,)] 0 tf_op_layer_strided_slice_1395[0]
tf_op_layer_strided_slice_1396[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_838/shape ( [(3,)] 0 tf_op_layer_strided_slice_1397[0]
tf_op_layer_strided_slice_1399[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_839/shape ( [(3,)] 0 tf_op_layer_strided_slice_1398[0]
tf_op_layer_strided_slice_1399[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_834 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_139[0][0]
tf_op_layer_Reshape_834/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_834/multiples [(3,)] 0 tf_op_layer_strided_slice_1392[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_835 (Tensor [(1, None, None)] 0 tf_op_layer_Add_139[0][0]
tf_op_layer_Reshape_835/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_835/multiples [(3,)] 0 tf_op_layer_strided_slice_1391[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_836 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_836/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_836/multiples [(3,)] 0 tf_op_layer_strided_slice_1395[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_837 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_837/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_837/multiples [(3,)] 0 tf_op_layer_strided_slice_1394[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_838 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_139[0][0]
tf_op_layer_Reshape_838/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_838/multiples [(3,)] 0 tf_op_layer_strided_slice_1398[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_839 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_839/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_839/multiples [(3,)] 0 tf_op_layer_strided_slice_1397[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_834 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_834[0][0]
tf_op_layer_Tile_834/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_835 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_835[0][0]
tf_op_layer_Tile_835/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_836 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_836[0][0]
tf_op_layer_Tile_836/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_837 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_837[0][0]
tf_op_layer_Tile_837/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_838 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_838[0][0]
tf_op_layer_Tile_838/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_839 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_839[0][0]
tf_op_layer_Tile_839/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_695 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_834[0][0]
tf_op_layer_Tile_835[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_696 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_836[0][0]
tf_op_layer_Tile_837[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_697 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_838[0][0]
tf_op_layer_Tile_839[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_556 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_695[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_557 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_696[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_558 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_697[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_973 (TensorFlo [(None, None)] 0 tf_op_layer_Square_556[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_974 (TensorFlo [(None, None)] 0 tf_op_layer_Square_557[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_975 (TensorFlo [(None, None)] 0 tf_op_layer_Square_558[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_417 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_973[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_417 (TensorFlo [()] 0 tf_op_layer_strided_slice_1393[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_418 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_974[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_418 (TensorFlo [()] 0 tf_op_layer_strided_slice_1396[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_419 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_975[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_419 (TensorFlo [()] 0 tf_op_layer_strided_slice_1399[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_417 (Tensor [(None, None)] 0 tf_op_layer_Neg_417[0][0]
tf_op_layer_Cast_417[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_418 (Tensor [(None, None)] 0 tf_op_layer_Neg_418[0][0]
tf_op_layer_Cast_418[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_419 (Tensor [(None, None)] 0 tf_op_layer_Neg_419[0][0]
tf_op_layer_Cast_419[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_417 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_417[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_418 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_418[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_419 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_419[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_976 (TensorFlo [()] 0 tf_op_layer_Exp_417[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_977 (TensorFlo [()] 0 tf_op_layer_Exp_418[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_978 (TensorFlo [()] 0 tf_op_layer_Exp_419[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_699 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_278 (TensorFl [()] 0 tf_op_layer_Mean_976[0][0]
tf_op_layer_Mean_977[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_418 (TensorFlow [()] 0 tf_op_layer_Mean_978[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_559 (TensorF [(None, 97)] 0 tf_op_layer_Sub_699[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_698 (TensorFlow [()] 0 tf_op_layer_AddV2_278[0][0]
tf_op_layer_Mul_418[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_139 (TensorFlow [(None,)] 0 tf_op_layer_Square_559[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_419 (TensorFlow [()] 0 tf_op_layer_Sub_698[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_279 (TensorFl [(None,)] 0 tf_op_layer_Sum_139[0][0]
tf_op_layer_Mul_419[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_979 (TensorFlo [()] 0 tf_op_layer_AddV2_279[0][0]
__________________________________________________________________________________________________
add_loss_139 (AddLoss) () 0 tf_op_layer_Mean_979[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 21s - loss: 8.2612WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0019s vs `on_train_batch_end` time: 0.1998s). Check your callbacks.
218/218 [==============================] - 1s 4ms/step - loss: 0.9070 - val_loss: 0.5129
Epoch 2/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4992 - val_loss: 0.4552
Epoch 3/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4387 - val_loss: 0.3844
Epoch 4/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3905 - val_loss: 0.3675
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3809 - val_loss: 0.3573
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3764 - val_loss: 0.3608
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3739 - val_loss: 0.3530
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3697 - val_loss: 0.3530
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3678 - val_loss: 0.3516
Epoch 10/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3646 - val_loss: 0.3458
Epoch 11/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3638 - val_loss: 0.3419
Epoch 12/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3623 - val_loss: 0.3446
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3607 - val_loss: 0.3439
Epoch 14/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3601 - val_loss: 0.3451
Epoch 15/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3602 - val_loss: 0.3411
Epoch 16/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3584 - val_loss: 0.3393
Epoch 17/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3573 - val_loss: 0.3376
Epoch 18/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3566 - val_loss: 0.3396
Epoch 19/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3570 - val_loss: 0.3388
Epoch 20/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3552 - val_loss: 0.3371
Epoch 21/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3550 - val_loss: 0.3352
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3546 - val_loss: 0.3381
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3548 - val_loss: 0.3375
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3544 - val_loss: 0.3379
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3540 - val_loss: 0.3378
Epoch 26/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3534 - val_loss: 0.3354
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3536 - val_loss: 0.3360
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3529 - val_loss: 0.3337
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3511 - val_loss: 0.3363
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3527 - val_loss: 0.3349
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3507 - val_loss: 0.3356
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3524 - val_loss: 0.3354
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3509 - val_loss: 0.3384
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3500 - val_loss: 0.3380
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3509 - val_loss: 0.3334
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3498 - val_loss: 0.3385
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3514 - val_loss: 0.3338
Epoch 38/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3496 - val_loss: 0.3359
Epoch 39/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3490 - val_loss: 0.3354
Epoch 40/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3498 - val_loss: 0.3333
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3324
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3478 - val_loss: 0.3330
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3331
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3326
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3329
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3486 - val_loss: 0.3346
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3311
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3484 - val_loss: 0.3359
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3471 - val_loss: 0.3349
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3327
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3474 - val_loss: 0.3346
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3484 - val_loss: 0.3335
Epoch 53/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3473 - val_loss: 0.3355
Epoch 54/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3481 - val_loss: 0.3323
Epoch 55/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3484 - val_loss: 0.3331
Epoch 56/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3483 - val_loss: 0.3325
Epoch 57/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3479 - val_loss: 0.3311
Epoch 58/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3458 - val_loss: 0.3330
Epoch 59/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3475 - val_loss: 0.3334
Epoch 60/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3465 - val_loss: 0.3307
Epoch 61/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3474 - val_loss: 0.3326
Epoch 62/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3461 - val_loss: 0.3297
Epoch 63/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3470 - val_loss: 0.3298
Epoch 64/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3478 - val_loss: 0.3320
Epoch 65/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3467 - val_loss: 0.3320
Epoch 66/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3462 - val_loss: 0.3301
Epoch 67/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3459 - val_loss: 0.3327
Epoch 68/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3467 - val_loss: 0.3305
Epoch 69/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3459 - val_loss: 0.3318
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3297
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3304
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3299
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3311
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3318
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3455 - val_loss: 0.3309
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3302
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3451 - val_loss: 0.3303
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3460 - val_loss: 0.3312
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3464 - val_loss: 0.3310
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3286
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3293
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3452 - val_loss: 0.3307
Epoch 83/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3450 - val_loss: 0.3272
Epoch 84/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3452 - val_loss: 0.3297
Epoch 85/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3460 - val_loss: 0.3327
Epoch 86/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3448 - val_loss: 0.3319
Epoch 87/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3454 - val_loss: 0.3285
Epoch 88/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3455 - val_loss: 0.3297
Epoch 89/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3443 - val_loss: 0.3329
Epoch 90/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3440 - val_loss: 0.3309
Epoch 91/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3446 - val_loss: 0.3318
Epoch 92/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3447 - val_loss: 0.3314
Epoch 93/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3449 - val_loss: 0.3294
Epoch 94/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3436 - val_loss: 0.3329
Epoch 95/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3443 - val_loss: 0.3324
Epoch 96/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3460 - val_loss: 0.3287
Epoch 97/100
218/218 [==============================] - 0s 2ms/step - loss: 0.3443 - val_loss: 0.3303
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3298
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3301
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3290
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_424 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1400 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1400 [()] 0 tf_op_layer_Shape_1400[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_140 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1400[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_140[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_420 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_140 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_420[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1401 (TensorF [(2,)] 0 tf_op_layer_Add_140[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1403 (TensorF [(2,)] 0 tf_op_layer_Add_140[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1402 (TensorF [(2,)] 0 tf_op_layer_Add_140[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1404 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1406 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1405 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1407 (TensorF [(2,)] 0 tf_op_layer_Add_140[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1409 (TensorF [(2,)] 0 tf_op_layer_Add_140[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1408 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1401 [()] 0 tf_op_layer_Shape_1401[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1403 [()] 0 tf_op_layer_Shape_1403[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1402 [()] 0 tf_op_layer_Shape_1402[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1404 [()] 0 tf_op_layer_Shape_1404[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1406 [()] 0 tf_op_layer_Shape_1406[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1405 [()] 0 tf_op_layer_Shape_1405[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1407 [()] 0 tf_op_layer_Shape_1407[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1409 [()] 0 tf_op_layer_Shape_1409[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1408 [()] 0 tf_op_layer_Shape_1408[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_840/shape ( [(3,)] 0 tf_op_layer_strided_slice_1401[0]
tf_op_layer_strided_slice_1403[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_841/shape ( [(3,)] 0 tf_op_layer_strided_slice_1402[0]
tf_op_layer_strided_slice_1403[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_842/shape ( [(3,)] 0 tf_op_layer_strided_slice_1404[0]
tf_op_layer_strided_slice_1406[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_843/shape ( [(3,)] 0 tf_op_layer_strided_slice_1405[0]
tf_op_layer_strided_slice_1406[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_844/shape ( [(3,)] 0 tf_op_layer_strided_slice_1407[0]
tf_op_layer_strided_slice_1409[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_845/shape ( [(3,)] 0 tf_op_layer_strided_slice_1408[0]
tf_op_layer_strided_slice_1409[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_840 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_140[0][0]
tf_op_layer_Reshape_840/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_840/multiples [(3,)] 0 tf_op_layer_strided_slice_1402[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_841 (Tensor [(1, None, None)] 0 tf_op_layer_Add_140[0][0]
tf_op_layer_Reshape_841/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_841/multiples [(3,)] 0 tf_op_layer_strided_slice_1401[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_842 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_842/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_842/multiples [(3,)] 0 tf_op_layer_strided_slice_1405[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_843 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_843/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_843/multiples [(3,)] 0 tf_op_layer_strided_slice_1404[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_844 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_140[0][0]
tf_op_layer_Reshape_844/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_844/multiples [(3,)] 0 tf_op_layer_strided_slice_1408[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_845 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_845/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_845/multiples [(3,)] 0 tf_op_layer_strided_slice_1407[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_840 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_840[0][0]
tf_op_layer_Tile_840/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_841 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_841[0][0]
tf_op_layer_Tile_841/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_842 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_842[0][0]
tf_op_layer_Tile_842/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_843 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_843[0][0]
tf_op_layer_Tile_843/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_844 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_844[0][0]
tf_op_layer_Tile_844/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_845 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_845[0][0]
tf_op_layer_Tile_845/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_700 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_840[0][0]
tf_op_layer_Tile_841[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_701 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_842[0][0]
tf_op_layer_Tile_843[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_702 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_844[0][0]
tf_op_layer_Tile_845[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_560 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_700[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_561 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_701[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_562 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_702[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_980 (TensorFlo [(None, None)] 0 tf_op_layer_Square_560[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_981 (TensorFlo [(None, None)] 0 tf_op_layer_Square_561[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_982 (TensorFlo [(None, None)] 0 tf_op_layer_Square_562[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_420 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_980[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_420 (TensorFlo [()] 0 tf_op_layer_strided_slice_1403[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_421 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_981[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_421 (TensorFlo [()] 0 tf_op_layer_strided_slice_1406[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_422 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_982[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_422 (TensorFlo [()] 0 tf_op_layer_strided_slice_1409[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_420 (Tensor [(None, None)] 0 tf_op_layer_Neg_420[0][0]
tf_op_layer_Cast_420[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_421 (Tensor [(None, None)] 0 tf_op_layer_Neg_421[0][0]
tf_op_layer_Cast_421[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_422 (Tensor [(None, None)] 0 tf_op_layer_Neg_422[0][0]
tf_op_layer_Cast_422[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_420 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_420[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_421 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_421[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_422 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_422[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_983 (TensorFlo [()] 0 tf_op_layer_Exp_420[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_984 (TensorFlo [()] 0 tf_op_layer_Exp_421[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_985 (TensorFlo [()] 0 tf_op_layer_Exp_422[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_704 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_280 (TensorFl [()] 0 tf_op_layer_Mean_983[0][0]
tf_op_layer_Mean_984[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_421 (TensorFlow [()] 0 tf_op_layer_Mean_985[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_563 (TensorF [(None, 97)] 0 tf_op_layer_Sub_704[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_703 (TensorFlow [()] 0 tf_op_layer_AddV2_280[0][0]
tf_op_layer_Mul_421[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_140 (TensorFlow [(None,)] 0 tf_op_layer_Square_563[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_422 (TensorFlow [()] 0 tf_op_layer_Sub_703[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_281 (TensorFl [(None,)] 0 tf_op_layer_Sum_140[0][0]
tf_op_layer_Mul_422[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_986 (TensorFlo [()] 0 tf_op_layer_AddV2_281[0][0]
__________________________________________________________________________________________________
add_loss_140 (AddLoss) () 0 tf_op_layer_Mean_986[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 26s - loss: 7.9448WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0016s vs `on_train_batch_end` time: 0.2442s). Check your callbacks.
218/218 [==============================] - 1s 5ms/step - loss: 1.9949 - val_loss: 1.0168
Epoch 2/100
218/218 [==============================] - 0s 2ms/step - loss: 0.8758 - val_loss: 0.7952
Epoch 3/100
218/218 [==============================] - 0s 2ms/step - loss: 0.7308 - val_loss: 0.7019
Epoch 4/100
218/218 [==============================] - 0s 2ms/step - loss: 0.6543 - val_loss: 0.6453
Epoch 5/100
218/218 [==============================] - 0s 2ms/step - loss: 0.6131 - val_loss: 0.6121
Epoch 6/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5873 - val_loss: 0.5835
Epoch 7/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5705 - val_loss: 0.5740
Epoch 8/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5564 - val_loss: 0.5670
Epoch 9/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5463 - val_loss: 0.5524
Epoch 10/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5362 - val_loss: 0.5500
Epoch 11/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5321 - val_loss: 0.5402
Epoch 12/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5213 - val_loss: 0.5325
Epoch 13/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5199 - val_loss: 0.5281
Epoch 14/100
218/218 [==============================] - 0s 2ms/step - loss: 0.5154 - val_loss: 0.5212
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5135 - val_loss: 0.5163
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5101 - val_loss: 0.5135
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5032 - val_loss: 0.5181
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.5007 - val_loss: 0.5108
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4999 - val_loss: 0.5142
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4971 - val_loss: 0.5052
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4927 - val_loss: 0.5056
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4905 - val_loss: 0.5013
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4914 - val_loss: 0.4999
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4894 - val_loss: 0.5036
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4862 - val_loss: 0.4963
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4851 - val_loss: 0.4946
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4833 - val_loss: 0.4936
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4808 - val_loss: 0.4911
Epoch 29/100
218/218 [==============================] - 0s 2ms/step - loss: 0.4823 - val_loss: 0.4918
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4822 - val_loss: 0.4916
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4793 - val_loss: 0.4883
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4777 - val_loss: 0.4862
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4777 - val_loss: 0.4913
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4778 - val_loss: 0.4893
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4745 - val_loss: 0.4818
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4758 - val_loss: 0.4869
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4758 - val_loss: 0.4903
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4754 - val_loss: 0.4870
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4721 - val_loss: 0.4920
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4734 - val_loss: 0.4855
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4711 - val_loss: 0.4791
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4722 - val_loss: 0.4840
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4708 - val_loss: 0.4822
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4726 - val_loss: 0.4785
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4727 - val_loss: 0.4816
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4691 - val_loss: 0.4831
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4689 - val_loss: 0.4776
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4685 - val_loss: 0.4798
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4678 - val_loss: 0.4848
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4702 - val_loss: 0.4755
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4680 - val_loss: 0.4823
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4663 - val_loss: 0.4774
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4659 - val_loss: 0.4739
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4648 - val_loss: 0.4794
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4653 - val_loss: 0.4739
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4666 - val_loss: 0.4781
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4660 - val_loss: 0.4789
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4646 - val_loss: 0.4719
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4658 - val_loss: 0.4752
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4639 - val_loss: 0.4755
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4627 - val_loss: 0.4733
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4640 - val_loss: 0.4748
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4647 - val_loss: 0.4712
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4645 - val_loss: 0.4739
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4637 - val_loss: 0.4748
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4624 - val_loss: 0.4742
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4622 - val_loss: 0.4756
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4613 - val_loss: 0.4756
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4623 - val_loss: 0.4733
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4606 - val_loss: 0.4764
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4625 - val_loss: 0.4705
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4613 - val_loss: 0.4735
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4602 - val_loss: 0.4709
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4614 - val_loss: 0.4686
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4595 - val_loss: 0.4743
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4627 - val_loss: 0.4704
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4595 - val_loss: 0.4749
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4606 - val_loss: 0.4726
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4603 - val_loss: 0.4701
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4605 - val_loss: 0.4726
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4600 - val_loss: 0.4673
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4579 - val_loss: 0.4709
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4596 - val_loss: 0.4659
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4578 - val_loss: 0.4693
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4594 - val_loss: 0.4682
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4577 - val_loss: 0.4674
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4583 - val_loss: 0.4698
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4566 - val_loss: 0.4662
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4593 - val_loss: 0.4704
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4598 - val_loss: 0.4662
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4582 - val_loss: 0.4693
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4582 - val_loss: 0.4700
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4572 - val_loss: 0.4671
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4567 - val_loss: 0.4736
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4578 - val_loss: 0.4723
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4573 - val_loss: 0.4722
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4551 - val_loss: 0.4680
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4576 - val_loss: 0.4711
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4584 - val_loss: 0.4670
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4559 - val_loss: 0.4681
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 17.79 seconds.
Calculating affinities...
Calculated affinities in 0.31 seconds.
Calculated graph and diffusion operator in 18.12 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 1.17 seconds.
Calculating KMeans...
Calculated KMeans in 15.28 seconds.
Calculated landmark operator in 17.92 seconds.
Calculating optimal t...
Automatically selected t = 33
Calculated optimal t in 2.21 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.47 seconds.
Calculating metric MDS...
Calculated metric MDS in 19.71 seconds.
Calculated PHATE in 58.45 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_425 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_426 (Dense) (None, 32) 2080 dense_425[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_426[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_426[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_427 (Dense) (None, 32) 128
_________________________________________________________________
dense_428 (Dense) (None, 64) 2112
_________________________________________________________________
dense_429 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_425 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_426 (Dense) (None, 32) 2080 dense_425[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_426[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_426[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1410 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1410 [()] 0 tf_op_layer_Shape_1410[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_141 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1410[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_141[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_423 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_141 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_423[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1411 (TensorF [(2,)] 0 tf_op_layer_Add_141[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1413 (TensorF [(2,)] 0 tf_op_layer_Add_141[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1412 (TensorF [(2,)] 0 tf_op_layer_Add_141[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1414 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1416 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1415 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1417 (TensorF [(2,)] 0 tf_op_layer_Add_141[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1419 (TensorF [(2,)] 0 tf_op_layer_Add_141[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1418 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1411 [()] 0 tf_op_layer_Shape_1411[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1413 [()] 0 tf_op_layer_Shape_1413[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1412 [()] 0 tf_op_layer_Shape_1412[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1414 [()] 0 tf_op_layer_Shape_1414[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1416 [()] 0 tf_op_layer_Shape_1416[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1415 [()] 0 tf_op_layer_Shape_1415[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1417 [()] 0 tf_op_layer_Shape_1417[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1419 [()] 0 tf_op_layer_Shape_1419[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1418 [()] 0 tf_op_layer_Shape_1418[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_846/shape ( [(3,)] 0 tf_op_layer_strided_slice_1411[0]
tf_op_layer_strided_slice_1413[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_847/shape ( [(3,)] 0 tf_op_layer_strided_slice_1412[0]
tf_op_layer_strided_slice_1413[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_848/shape ( [(3,)] 0 tf_op_layer_strided_slice_1414[0]
tf_op_layer_strided_slice_1416[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_849/shape ( [(3,)] 0 tf_op_layer_strided_slice_1415[0]
tf_op_layer_strided_slice_1416[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_850/shape ( [(3,)] 0 tf_op_layer_strided_slice_1417[0]
tf_op_layer_strided_slice_1419[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_851/shape ( [(3,)] 0 tf_op_layer_strided_slice_1418[0]
tf_op_layer_strided_slice_1419[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_846 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_141[0][0]
tf_op_layer_Reshape_846/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_846/multiples [(3,)] 0 tf_op_layer_strided_slice_1412[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_847 (Tensor [(1, None, None)] 0 tf_op_layer_Add_141[0][0]
tf_op_layer_Reshape_847/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_847/multiples [(3,)] 0 tf_op_layer_strided_slice_1411[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_848 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_848/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_848/multiples [(3,)] 0 tf_op_layer_strided_slice_1415[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_849 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_849/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_849/multiples [(3,)] 0 tf_op_layer_strided_slice_1414[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_850 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_141[0][0]
tf_op_layer_Reshape_850/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_850/multiples [(3,)] 0 tf_op_layer_strided_slice_1418[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_851 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_851/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_851/multiples [(3,)] 0 tf_op_layer_strided_slice_1417[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_846 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_846[0][0]
tf_op_layer_Tile_846/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_847 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_847[0][0]
tf_op_layer_Tile_847/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_848 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_848[0][0]
tf_op_layer_Tile_848/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_849 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_849[0][0]
tf_op_layer_Tile_849/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_850 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_850[0][0]
tf_op_layer_Tile_850/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_851 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_851[0][0]
tf_op_layer_Tile_851/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_705 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_846[0][0]
tf_op_layer_Tile_847[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_706 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_848[0][0]
tf_op_layer_Tile_849[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_707 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_850[0][0]
tf_op_layer_Tile_851[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_564 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_705[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_565 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_706[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_566 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_707[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_987 (TensorFlo [(None, None)] 0 tf_op_layer_Square_564[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_988 (TensorFlo [(None, None)] 0 tf_op_layer_Square_565[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_989 (TensorFlo [(None, None)] 0 tf_op_layer_Square_566[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_423 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_987[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_423 (TensorFlo [()] 0 tf_op_layer_strided_slice_1413[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_424 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_988[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_424 (TensorFlo [()] 0 tf_op_layer_strided_slice_1416[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_425 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_989[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_425 (TensorFlo [()] 0 tf_op_layer_strided_slice_1419[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_423 (Tensor [(None, None)] 0 tf_op_layer_Neg_423[0][0]
tf_op_layer_Cast_423[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_424 (Tensor [(None, None)] 0 tf_op_layer_Neg_424[0][0]
tf_op_layer_Cast_424[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_425 (Tensor [(None, None)] 0 tf_op_layer_Neg_425[0][0]
tf_op_layer_Cast_425[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_423 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_423[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_424 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_424[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_425 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_425[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_990 (TensorFlo [()] 0 tf_op_layer_Exp_423[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_991 (TensorFlo [()] 0 tf_op_layer_Exp_424[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_992 (TensorFlo [()] 0 tf_op_layer_Exp_425[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_709 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_282 (TensorFl [()] 0 tf_op_layer_Mean_990[0][0]
tf_op_layer_Mean_991[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_424 (TensorFlow [()] 0 tf_op_layer_Mean_992[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_567 (TensorF [(None, 97)] 0 tf_op_layer_Sub_709[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_708 (TensorFlow [()] 0 tf_op_layer_AddV2_282[0][0]
tf_op_layer_Mul_424[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_141 (TensorFlow [(None,)] 0 tf_op_layer_Square_567[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_425 (TensorFlow [()] 0 tf_op_layer_Sub_708[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_283 (TensorFl [(None,)] 0 tf_op_layer_Sum_141[0][0]
tf_op_layer_Mul_425[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_993 (TensorFlo [()] 0 tf_op_layer_AddV2_283[0][0]
__________________________________________________________________________________________________
add_loss_141 (AddLoss) () 0 tf_op_layer_Mean_993[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 8.0894WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.1033s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9301 - val_loss: 0.4737
Epoch 2/100
218/218 [==============================] - 0s 982us/step - loss: 0.4339 - val_loss: 0.3873
Epoch 3/100
218/218 [==============================] - 0s 970us/step - loss: 0.3873 - val_loss: 0.3653
Epoch 4/100
218/218 [==============================] - 0s 980us/step - loss: 0.3793 - val_loss: 0.3601
Epoch 5/100
218/218 [==============================] - 0s 975us/step - loss: 0.3721 - val_loss: 0.3553
Epoch 6/100
218/218 [==============================] - 0s 971us/step - loss: 0.3691 - val_loss: 0.3552
Epoch 7/100
218/218 [==============================] - 0s 991us/step - loss: 0.3663 - val_loss: 0.3518
Epoch 8/100
218/218 [==============================] - 0s 955us/step - loss: 0.3650 - val_loss: 0.3516
Epoch 9/100
218/218 [==============================] - 0s 996us/step - loss: 0.3611 - val_loss: 0.3470
Epoch 10/100
218/218 [==============================] - 0s 977us/step - loss: 0.3600 - val_loss: 0.3443
Epoch 11/100
218/218 [==============================] - 0s 991us/step - loss: 0.3594 - val_loss: 0.3431
Epoch 12/100
218/218 [==============================] - 0s 985us/step - loss: 0.3583 - val_loss: 0.3431
Epoch 13/100
218/218 [==============================] - 0s 972us/step - loss: 0.3562 - val_loss: 0.3416
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3552 - val_loss: 0.3439
Epoch 15/100
218/218 [==============================] - 0s 987us/step - loss: 0.3532 - val_loss: 0.3432
Epoch 16/100
218/218 [==============================] - 0s 971us/step - loss: 0.3533 - val_loss: 0.3404
Epoch 17/100
218/218 [==============================] - 0s 974us/step - loss: 0.3546 - val_loss: 0.3414
Epoch 18/100
218/218 [==============================] - 0s 975us/step - loss: 0.3528 - val_loss: 0.3368
Epoch 19/100
218/218 [==============================] - 0s 975us/step - loss: 0.3531 - val_loss: 0.3405
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3523 - val_loss: 0.3430
Epoch 21/100
218/218 [==============================] - 0s 970us/step - loss: 0.3510 - val_loss: 0.3412
Epoch 22/100
218/218 [==============================] - 0s 977us/step - loss: 0.3518 - val_loss: 0.3374
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3510 - val_loss: 0.3386
Epoch 24/100
218/218 [==============================] - 0s 971us/step - loss: 0.3502 - val_loss: 0.3365
Epoch 25/100
218/218 [==============================] - 0s 976us/step - loss: 0.3494 - val_loss: 0.3365
Epoch 26/100
218/218 [==============================] - 0s 978us/step - loss: 0.3503 - val_loss: 0.3369
Epoch 27/100
218/218 [==============================] - 0s 975us/step - loss: 0.3493 - val_loss: 0.3352
Epoch 28/100
218/218 [==============================] - 0s 980us/step - loss: 0.3494 - val_loss: 0.3365
Epoch 29/100
218/218 [==============================] - 0s 974us/step - loss: 0.3477 - val_loss: 0.3358
Epoch 30/100
218/218 [==============================] - 0s 976us/step - loss: 0.3474 - val_loss: 0.3362
Epoch 31/100
218/218 [==============================] - 0s 981us/step - loss: 0.3473 - val_loss: 0.3335
Epoch 32/100
218/218 [==============================] - 0s 990us/step - loss: 0.3474 - val_loss: 0.3392
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3345
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3340
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3336
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3383
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3455 - val_loss: 0.3337
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3349
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3461 - val_loss: 0.3328
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3343
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3359
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3323
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3318
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3306
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3347
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3317
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3329
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3341
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3321
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3314
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3323
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3348
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3326
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3326
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3324
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3321
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3308
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3319
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3323
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3313
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3312
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3285
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3317
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3321
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3405 - val_loss: 0.3315
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3296
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3326
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3300
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3299
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3320
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3304
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3311
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3310
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3300
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3310
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3395 - val_loss: 0.3283
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3302
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3260
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3317
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3402 - val_loss: 0.3281
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3390 - val_loss: 0.3297
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3319
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3406 - val_loss: 0.3318
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3381 - val_loss: 0.3319
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3296
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3316
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3401 - val_loss: 0.3315
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3390 - val_loss: 0.3287
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3291
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3279
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3390 - val_loss: 0.3306
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3390 - val_loss: 0.3281
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3389 - val_loss: 0.3295
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3269
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3392 - val_loss: 0.3292
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3389 - val_loss: 0.3302
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3387 - val_loss: 0.3305
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3383 - val_loss: 0.3270
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3400 - val_loss: 0.3289
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3391 - val_loss: 0.3286
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_430 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1420 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1420 [()] 0 tf_op_layer_Shape_1420[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_142 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1420[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_142[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_426 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_142 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_426[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1421 (TensorF [(2,)] 0 tf_op_layer_Add_142[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1423 (TensorF [(2,)] 0 tf_op_layer_Add_142[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1422 (TensorF [(2,)] 0 tf_op_layer_Add_142[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1424 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1426 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1425 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1427 (TensorF [(2,)] 0 tf_op_layer_Add_142[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1429 (TensorF [(2,)] 0 tf_op_layer_Add_142[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1428 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1421 [()] 0 tf_op_layer_Shape_1421[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1423 [()] 0 tf_op_layer_Shape_1423[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1422 [()] 0 tf_op_layer_Shape_1422[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1424 [()] 0 tf_op_layer_Shape_1424[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1426 [()] 0 tf_op_layer_Shape_1426[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1425 [()] 0 tf_op_layer_Shape_1425[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1427 [()] 0 tf_op_layer_Shape_1427[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1429 [()] 0 tf_op_layer_Shape_1429[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1428 [()] 0 tf_op_layer_Shape_1428[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_852/shape ( [(3,)] 0 tf_op_layer_strided_slice_1421[0]
tf_op_layer_strided_slice_1423[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_853/shape ( [(3,)] 0 tf_op_layer_strided_slice_1422[0]
tf_op_layer_strided_slice_1423[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_854/shape ( [(3,)] 0 tf_op_layer_strided_slice_1424[0]
tf_op_layer_strided_slice_1426[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_855/shape ( [(3,)] 0 tf_op_layer_strided_slice_1425[0]
tf_op_layer_strided_slice_1426[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_856/shape ( [(3,)] 0 tf_op_layer_strided_slice_1427[0]
tf_op_layer_strided_slice_1429[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_857/shape ( [(3,)] 0 tf_op_layer_strided_slice_1428[0]
tf_op_layer_strided_slice_1429[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_852 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_142[0][0]
tf_op_layer_Reshape_852/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_852/multiples [(3,)] 0 tf_op_layer_strided_slice_1422[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_853 (Tensor [(1, None, None)] 0 tf_op_layer_Add_142[0][0]
tf_op_layer_Reshape_853/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_853/multiples [(3,)] 0 tf_op_layer_strided_slice_1421[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_854 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_854/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_854/multiples [(3,)] 0 tf_op_layer_strided_slice_1425[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_855 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_855/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_855/multiples [(3,)] 0 tf_op_layer_strided_slice_1424[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_856 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_142[0][0]
tf_op_layer_Reshape_856/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_856/multiples [(3,)] 0 tf_op_layer_strided_slice_1428[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_857 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_857/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_857/multiples [(3,)] 0 tf_op_layer_strided_slice_1427[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_852 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_852[0][0]
tf_op_layer_Tile_852/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_853 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_853[0][0]
tf_op_layer_Tile_853/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_854 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_854[0][0]
tf_op_layer_Tile_854/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_855 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_855[0][0]
tf_op_layer_Tile_855/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_856 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_856[0][0]
tf_op_layer_Tile_856/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_857 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_857[0][0]
tf_op_layer_Tile_857/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_710 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_852[0][0]
tf_op_layer_Tile_853[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_711 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_854[0][0]
tf_op_layer_Tile_855[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_712 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_856[0][0]
tf_op_layer_Tile_857[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_568 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_710[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_569 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_711[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_570 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_712[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_994 (TensorFlo [(None, None)] 0 tf_op_layer_Square_568[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_995 (TensorFlo [(None, None)] 0 tf_op_layer_Square_569[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_996 (TensorFlo [(None, None)] 0 tf_op_layer_Square_570[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_426 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_994[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_426 (TensorFlo [()] 0 tf_op_layer_strided_slice_1423[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_427 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_995[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_427 (TensorFlo [()] 0 tf_op_layer_strided_slice_1426[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_428 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_996[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_428 (TensorFlo [()] 0 tf_op_layer_strided_slice_1429[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_426 (Tensor [(None, None)] 0 tf_op_layer_Neg_426[0][0]
tf_op_layer_Cast_426[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_427 (Tensor [(None, None)] 0 tf_op_layer_Neg_427[0][0]
tf_op_layer_Cast_427[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_428 (Tensor [(None, None)] 0 tf_op_layer_Neg_428[0][0]
tf_op_layer_Cast_428[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_426 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_426[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_427 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_427[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_428 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_428[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_997 (TensorFlo [()] 0 tf_op_layer_Exp_426[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_998 (TensorFlo [()] 0 tf_op_layer_Exp_427[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_999 (TensorFlo [()] 0 tf_op_layer_Exp_428[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_714 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_284 (TensorFl [()] 0 tf_op_layer_Mean_997[0][0]
tf_op_layer_Mean_998[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_427 (TensorFlow [()] 0 tf_op_layer_Mean_999[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_571 (TensorF [(None, 97)] 0 tf_op_layer_Sub_714[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_713 (TensorFlow [()] 0 tf_op_layer_AddV2_284[0][0]
tf_op_layer_Mul_427[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_142 (TensorFlow [(None,)] 0 tf_op_layer_Square_571[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_428 (TensorFlow [()] 0 tf_op_layer_Sub_713[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_285 (TensorFl [(None,)] 0 tf_op_layer_Sum_142[0][0]
tf_op_layer_Mul_428[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1000 (TensorFl [()] 0 tf_op_layer_AddV2_285[0][0]
__________________________________________________________________________________________________
add_loss_142 (AddLoss) () 0 tf_op_layer_Mean_1000[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 11s - loss: 8.2209WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.1102s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 2.0837 - val_loss: 0.9883
Epoch 2/100
218/218 [==============================] - 0s 894us/step - loss: 0.8885 - val_loss: 0.8042
Epoch 3/100
218/218 [==============================] - 0s 886us/step - loss: 0.7506 - val_loss: 0.6949
Epoch 4/100
218/218 [==============================] - 0s 884us/step - loss: 0.6637 - val_loss: 0.6344
Epoch 5/100
218/218 [==============================] - 0s 890us/step - loss: 0.6219 - val_loss: 0.6008
Epoch 6/100
218/218 [==============================] - 0s 899us/step - loss: 0.5990 - val_loss: 0.5903
Epoch 7/100
218/218 [==============================] - 0s 903us/step - loss: 0.5827 - val_loss: 0.5800
Epoch 8/100
218/218 [==============================] - 0s 903us/step - loss: 0.5676 - val_loss: 0.5633
Epoch 9/100
218/218 [==============================] - 0s 895us/step - loss: 0.5571 - val_loss: 0.5492
Epoch 10/100
218/218 [==============================] - 0s 896us/step - loss: 0.5466 - val_loss: 0.5400
Epoch 11/100
218/218 [==============================] - 0s 899us/step - loss: 0.5431 - val_loss: 0.5417
Epoch 12/100
218/218 [==============================] - 0s 907us/step - loss: 0.5375 - val_loss: 0.5299
Epoch 13/100
218/218 [==============================] - 0s 898us/step - loss: 0.5297 - val_loss: 0.5260
Epoch 14/100
218/218 [==============================] - 0s 898us/step - loss: 0.5244 - val_loss: 0.5232
Epoch 15/100
218/218 [==============================] - 0s 898us/step - loss: 0.5201 - val_loss: 0.5213
Epoch 16/100
218/218 [==============================] - 0s 901us/step - loss: 0.5168 - val_loss: 0.5148
Epoch 17/100
218/218 [==============================] - 0s 897us/step - loss: 0.5131 - val_loss: 0.5120
Epoch 18/100
218/218 [==============================] - 0s 894us/step - loss: 0.5095 - val_loss: 0.5129
Epoch 19/100
218/218 [==============================] - 0s 922us/step - loss: 0.5078 - val_loss: 0.5029
Epoch 20/100
218/218 [==============================] - 0s 871us/step - loss: 0.5034 - val_loss: 0.5041
Epoch 21/100
218/218 [==============================] - 0s 907us/step - loss: 0.4998 - val_loss: 0.5022
Epoch 22/100
218/218 [==============================] - 0s 871us/step - loss: 0.4978 - val_loss: 0.5027
Epoch 23/100
218/218 [==============================] - 0s 906us/step - loss: 0.4965 - val_loss: 0.5021
Epoch 24/100
218/218 [==============================] - 0s 898us/step - loss: 0.4955 - val_loss: 0.4998
Epoch 25/100
218/218 [==============================] - 0s 895us/step - loss: 0.4945 - val_loss: 0.4925
Epoch 26/100
218/218 [==============================] - 0s 910us/step - loss: 0.4911 - val_loss: 0.5032
Epoch 27/100
218/218 [==============================] - 0s 905us/step - loss: 0.4916 - val_loss: 0.4860
Epoch 28/100
218/218 [==============================] - 0s 897us/step - loss: 0.4880 - val_loss: 0.4904
Epoch 29/100
218/218 [==============================] - 0s 899us/step - loss: 0.4889 - val_loss: 0.4900
Epoch 30/100
218/218 [==============================] - 0s 895us/step - loss: 0.4854 - val_loss: 0.4869
Epoch 31/100
218/218 [==============================] - 0s 897us/step - loss: 0.4847 - val_loss: 0.4845
Epoch 32/100
218/218 [==============================] - 0s 897us/step - loss: 0.4847 - val_loss: 0.4787
Epoch 33/100
218/218 [==============================] - 0s 891us/step - loss: 0.4852 - val_loss: 0.4899
Epoch 34/100
218/218 [==============================] - 0s 895us/step - loss: 0.4834 - val_loss: 0.4851
Epoch 35/100
218/218 [==============================] - 0s 896us/step - loss: 0.4814 - val_loss: 0.4776
Epoch 36/100
218/218 [==============================] - 0s 890us/step - loss: 0.4793 - val_loss: 0.4826
Epoch 37/100
218/218 [==============================] - 0s 896us/step - loss: 0.4776 - val_loss: 0.4838
Epoch 38/100
218/218 [==============================] - 0s 896us/step - loss: 0.4792 - val_loss: 0.4802
Epoch 39/100
218/218 [==============================] - 0s 909us/step - loss: 0.4794 - val_loss: 0.4847
Epoch 40/100
218/218 [==============================] - 0s 901us/step - loss: 0.4764 - val_loss: 0.4833
Epoch 41/100
218/218 [==============================] - 0s 894us/step - loss: 0.4758 - val_loss: 0.4779
Epoch 42/100
218/218 [==============================] - 0s 899us/step - loss: 0.4768 - val_loss: 0.4784
Epoch 43/100
218/218 [==============================] - 0s 897us/step - loss: 0.4768 - val_loss: 0.4820
Epoch 44/100
218/218 [==============================] - 0s 897us/step - loss: 0.4749 - val_loss: 0.4780
Epoch 45/100
218/218 [==============================] - 0s 896us/step - loss: 0.4758 - val_loss: 0.4800
Epoch 46/100
218/218 [==============================] - 0s 912us/step - loss: 0.4730 - val_loss: 0.4818
Epoch 47/100
218/218 [==============================] - 0s 907us/step - loss: 0.4743 - val_loss: 0.4779
Epoch 48/100
218/218 [==============================] - 0s 898us/step - loss: 0.4723 - val_loss: 0.4790
Epoch 49/100
218/218 [==============================] - 0s 897us/step - loss: 0.4729 - val_loss: 0.4730
Epoch 50/100
218/218 [==============================] - 0s 894us/step - loss: 0.4705 - val_loss: 0.4748
Epoch 51/100
218/218 [==============================] - 0s 897us/step - loss: 0.4705 - val_loss: 0.4781
Epoch 52/100
218/218 [==============================] - 0s 898us/step - loss: 0.4709 - val_loss: 0.4762
Epoch 53/100
218/218 [==============================] - 0s 895us/step - loss: 0.4704 - val_loss: 0.4759
Epoch 54/100
218/218 [==============================] - 0s 894us/step - loss: 0.4709 - val_loss: 0.4779
Epoch 55/100
218/218 [==============================] - 0s 894us/step - loss: 0.4708 - val_loss: 0.4736
Epoch 56/100
218/218 [==============================] - 0s 896us/step - loss: 0.4707 - val_loss: 0.4720
Epoch 57/100
218/218 [==============================] - 0s 899us/step - loss: 0.4698 - val_loss: 0.4755
Epoch 58/100
218/218 [==============================] - 0s 910us/step - loss: 0.4680 - val_loss: 0.4749
Epoch 59/100
218/218 [==============================] - 0s 899us/step - loss: 0.4695 - val_loss: 0.4771
Epoch 60/100
218/218 [==============================] - 0s 895us/step - loss: 0.4673 - val_loss: 0.4737
Epoch 61/100
218/218 [==============================] - 0s 906us/step - loss: 0.4689 - val_loss: 0.4737
Epoch 62/100
218/218 [==============================] - 0s 910us/step - loss: 0.4654 - val_loss: 0.4743
Epoch 63/100
218/218 [==============================] - 0s 899us/step - loss: 0.4676 - val_loss: 0.4697
Epoch 64/100
218/218 [==============================] - 0s 894us/step - loss: 0.4680 - val_loss: 0.4741
Epoch 65/100
218/218 [==============================] - 0s 899us/step - loss: 0.4675 - val_loss: 0.4706
Epoch 66/100
218/218 [==============================] - 0s 897us/step - loss: 0.4674 - val_loss: 0.4705
Epoch 67/100
218/218 [==============================] - 0s 897us/step - loss: 0.4670 - val_loss: 0.4685
Epoch 68/100
218/218 [==============================] - 0s 899us/step - loss: 0.4658 - val_loss: 0.4733
Epoch 69/100
218/218 [==============================] - 0s 898us/step - loss: 0.4664 - val_loss: 0.4702
Epoch 70/100
218/218 [==============================] - 0s 897us/step - loss: 0.4642 - val_loss: 0.4732
Epoch 71/100
218/218 [==============================] - 0s 894us/step - loss: 0.4629 - val_loss: 0.4721
Epoch 72/100
218/218 [==============================] - 0s 897us/step - loss: 0.4656 - val_loss: 0.4680
Epoch 73/100
218/218 [==============================] - 0s 901us/step - loss: 0.4657 - val_loss: 0.4723
Epoch 74/100
218/218 [==============================] - 0s 899us/step - loss: 0.4654 - val_loss: 0.4693
Epoch 75/100
218/218 [==============================] - 0s 913us/step - loss: 0.4633 - val_loss: 0.4691
Epoch 76/100
218/218 [==============================] - 0s 899us/step - loss: 0.4649 - val_loss: 0.4655
Epoch 77/100
218/218 [==============================] - 0s 897us/step - loss: 0.4644 - val_loss: 0.4679
Epoch 78/100
218/218 [==============================] - 0s 894us/step - loss: 0.4630 - val_loss: 0.4656
Epoch 79/100
218/218 [==============================] - 0s 893us/step - loss: 0.4635 - val_loss: 0.4703
Epoch 80/100
218/218 [==============================] - 0s 894us/step - loss: 0.4629 - val_loss: 0.4723
Epoch 81/100
218/218 [==============================] - 0s 894us/step - loss: 0.4633 - val_loss: 0.4693
Epoch 82/100
218/218 [==============================] - 0s 895us/step - loss: 0.4626 - val_loss: 0.4708
Epoch 83/100
218/218 [==============================] - 0s 891us/step - loss: 0.4628 - val_loss: 0.4663
Epoch 84/100
218/218 [==============================] - 0s 916us/step - loss: 0.4609 - val_loss: 0.4649
Epoch 85/100
218/218 [==============================] - 0s 901us/step - loss: 0.4620 - val_loss: 0.4684
Epoch 86/100
218/218 [==============================] - 0s 896us/step - loss: 0.4643 - val_loss: 0.4667
Epoch 87/100
218/218 [==============================] - 0s 896us/step - loss: 0.4602 - val_loss: 0.4710
Epoch 88/100
218/218 [==============================] - 0s 909us/step - loss: 0.4618 - val_loss: 0.4664
Epoch 89/100
218/218 [==============================] - 0s 905us/step - loss: 0.4616 - val_loss: 0.4681
Epoch 90/100
218/218 [==============================] - 0s 905us/step - loss: 0.4601 - val_loss: 0.4656
Epoch 91/100
218/218 [==============================] - 0s 909us/step - loss: 0.4610 - val_loss: 0.4637
Epoch 92/100
218/218 [==============================] - 0s 906us/step - loss: 0.4626 - val_loss: 0.4665
Epoch 93/100
218/218 [==============================] - 0s 901us/step - loss: 0.4605 - val_loss: 0.4626
Epoch 94/100
218/218 [==============================] - 0s 892us/step - loss: 0.4597 - val_loss: 0.4655
Epoch 95/100
218/218 [==============================] - 0s 906us/step - loss: 0.4617 - val_loss: 0.4637
Epoch 96/100
218/218 [==============================] - 0s 925us/step - loss: 0.4607 - val_loss: 0.4681
Epoch 97/100
218/218 [==============================] - 0s 926us/step - loss: 0.4608 - val_loss: 0.4621
Epoch 98/100
218/218 [==============================] - 0s 938us/step - loss: 0.4597 - val_loss: 0.4640
Epoch 99/100
218/218 [==============================] - 0s 926us/step - loss: 0.4613 - val_loss: 0.4628
Epoch 100/100
218/218 [==============================] - 0s 925us/step - loss: 0.4608 - val_loss: 0.4656
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.03 seconds.
Calculating affinities...
Calculated affinities in 0.25 seconds.
Calculated graph and diffusion operator in 13.30 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.80 seconds.
Calculating KMeans...
Calculated KMeans in 13.24 seconds.
Calculated landmark operator in 15.45 seconds.
Calculating optimal t...
Automatically selected t = 32
Calculated optimal t in 2.33 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.40 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.46 seconds.
Calculated PHATE in 47.96 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_431 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_432 (Dense) (None, 32) 2080 dense_431[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_432[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_432[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_433 (Dense) (None, 32) 128
_________________________________________________________________
dense_434 (Dense) (None, 64) 2112
_________________________________________________________________
dense_435 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_431 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_432 (Dense) (None, 32) 2080 dense_431[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_432[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_432[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1430 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1430 [()] 0 tf_op_layer_Shape_1430[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_143 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1430[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_143[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_429 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_143 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_429[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1431 (TensorF [(2,)] 0 tf_op_layer_Add_143[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1433 (TensorF [(2,)] 0 tf_op_layer_Add_143[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1432 (TensorF [(2,)] 0 tf_op_layer_Add_143[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1434 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1436 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1435 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1437 (TensorF [(2,)] 0 tf_op_layer_Add_143[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1439 (TensorF [(2,)] 0 tf_op_layer_Add_143[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1438 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1431 [()] 0 tf_op_layer_Shape_1431[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1433 [()] 0 tf_op_layer_Shape_1433[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1432 [()] 0 tf_op_layer_Shape_1432[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1434 [()] 0 tf_op_layer_Shape_1434[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1436 [()] 0 tf_op_layer_Shape_1436[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1435 [()] 0 tf_op_layer_Shape_1435[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1437 [()] 0 tf_op_layer_Shape_1437[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1439 [()] 0 tf_op_layer_Shape_1439[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1438 [()] 0 tf_op_layer_Shape_1438[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_858/shape ( [(3,)] 0 tf_op_layer_strided_slice_1431[0]
tf_op_layer_strided_slice_1433[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_859/shape ( [(3,)] 0 tf_op_layer_strided_slice_1432[0]
tf_op_layer_strided_slice_1433[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_860/shape ( [(3,)] 0 tf_op_layer_strided_slice_1434[0]
tf_op_layer_strided_slice_1436[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_861/shape ( [(3,)] 0 tf_op_layer_strided_slice_1435[0]
tf_op_layer_strided_slice_1436[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_862/shape ( [(3,)] 0 tf_op_layer_strided_slice_1437[0]
tf_op_layer_strided_slice_1439[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_863/shape ( [(3,)] 0 tf_op_layer_strided_slice_1438[0]
tf_op_layer_strided_slice_1439[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_858 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_143[0][0]
tf_op_layer_Reshape_858/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_858/multiples [(3,)] 0 tf_op_layer_strided_slice_1432[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_859 (Tensor [(1, None, None)] 0 tf_op_layer_Add_143[0][0]
tf_op_layer_Reshape_859/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_859/multiples [(3,)] 0 tf_op_layer_strided_slice_1431[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_860 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_860/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_860/multiples [(3,)] 0 tf_op_layer_strided_slice_1435[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_861 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_861/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_861/multiples [(3,)] 0 tf_op_layer_strided_slice_1434[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_862 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_143[0][0]
tf_op_layer_Reshape_862/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_862/multiples [(3,)] 0 tf_op_layer_strided_slice_1438[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_863 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_863/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_863/multiples [(3,)] 0 tf_op_layer_strided_slice_1437[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_858 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_858[0][0]
tf_op_layer_Tile_858/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_859 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_859[0][0]
tf_op_layer_Tile_859/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_860 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_860[0][0]
tf_op_layer_Tile_860/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_861 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_861[0][0]
tf_op_layer_Tile_861/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_862 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_862[0][0]
tf_op_layer_Tile_862/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_863 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_863[0][0]
tf_op_layer_Tile_863/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_715 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_858[0][0]
tf_op_layer_Tile_859[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_716 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_860[0][0]
tf_op_layer_Tile_861[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_717 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_862[0][0]
tf_op_layer_Tile_863[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_572 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_715[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_573 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_716[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_574 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_717[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1001 (TensorFl [(None, None)] 0 tf_op_layer_Square_572[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1002 (TensorFl [(None, None)] 0 tf_op_layer_Square_573[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1003 (TensorFl [(None, None)] 0 tf_op_layer_Square_574[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_429 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1001[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_429 (TensorFlo [()] 0 tf_op_layer_strided_slice_1433[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_430 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1002[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_430 (TensorFlo [()] 0 tf_op_layer_strided_slice_1436[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_431 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1003[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_431 (TensorFlo [()] 0 tf_op_layer_strided_slice_1439[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_429 (Tensor [(None, None)] 0 tf_op_layer_Neg_429[0][0]
tf_op_layer_Cast_429[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_430 (Tensor [(None, None)] 0 tf_op_layer_Neg_430[0][0]
tf_op_layer_Cast_430[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_431 (Tensor [(None, None)] 0 tf_op_layer_Neg_431[0][0]
tf_op_layer_Cast_431[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_429 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_429[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_430 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_430[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_431 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_431[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1004 (TensorFl [()] 0 tf_op_layer_Exp_429[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1005 (TensorFl [()] 0 tf_op_layer_Exp_430[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1006 (TensorFl [()] 0 tf_op_layer_Exp_431[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_719 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_286 (TensorFl [()] 0 tf_op_layer_Mean_1004[0][0]
tf_op_layer_Mean_1005[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_430 (TensorFlow [()] 0 tf_op_layer_Mean_1006[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_575 (TensorF [(None, 97)] 0 tf_op_layer_Sub_719[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_718 (TensorFlow [()] 0 tf_op_layer_AddV2_286[0][0]
tf_op_layer_Mul_430[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_143 (TensorFlow [(None,)] 0 tf_op_layer_Square_575[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_431 (TensorFlow [()] 0 tf_op_layer_Sub_718[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_287 (TensorFl [(None,)] 0 tf_op_layer_Sum_143[0][0]
tf_op_layer_Mul_431[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1007 (TensorFl [()] 0 tf_op_layer_AddV2_287[0][0]
__________________________________________________________________________________________________
add_loss_143 (AddLoss) () 0 tf_op_layer_Mean_1007[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 7.3600WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0973s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 0.8367 - val_loss: 0.4272
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4091 - val_loss: 0.3798
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3840 - val_loss: 0.3704
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3765 - val_loss: 0.3614
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3713 - val_loss: 0.3604
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3682 - val_loss: 0.3590
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3653 - val_loss: 0.3533
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3640 - val_loss: 0.3575
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3609 - val_loss: 0.3543
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3595 - val_loss: 0.3524
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3591 - val_loss: 0.3519
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3574 - val_loss: 0.3506
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3583 - val_loss: 0.3470
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3558 - val_loss: 0.3454
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3538 - val_loss: 0.3486
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3542 - val_loss: 0.3487
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3523 - val_loss: 0.3463
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3534 - val_loss: 0.3442
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3519 - val_loss: 0.3439
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3509 - val_loss: 0.3464
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3505 - val_loss: 0.3438
Epoch 22/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3501 - val_loss: 0.3468
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3503 - val_loss: 0.3444
Epoch 24/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3451
Epoch 25/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3499 - val_loss: 0.3453
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3437
Epoch 27/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3491 - val_loss: 0.3454
Epoch 28/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3405
Epoch 29/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3487 - val_loss: 0.3397
Epoch 30/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3445
Epoch 31/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3477 - val_loss: 0.3412
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3415
Epoch 33/100
218/218 [==============================] - 0s 988us/step - loss: 0.3466 - val_loss: 0.3403
Epoch 34/100
218/218 [==============================] - 0s 985us/step - loss: 0.3460 - val_loss: 0.3427
Epoch 35/100
218/218 [==============================] - 0s 984us/step - loss: 0.3473 - val_loss: 0.3411
Epoch 36/100
218/218 [==============================] - 0s 980us/step - loss: 0.3456 - val_loss: 0.3415
Epoch 37/100
218/218 [==============================] - 0s 990us/step - loss: 0.3472 - val_loss: 0.3397
Epoch 38/100
218/218 [==============================] - 0s 983us/step - loss: 0.3463 - val_loss: 0.3407
Epoch 39/100
218/218 [==============================] - 0s 983us/step - loss: 0.3472 - val_loss: 0.3440
Epoch 40/100
218/218 [==============================] - 0s 984us/step - loss: 0.3462 - val_loss: 0.3402
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3381
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3458 - val_loss: 0.3402
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3379
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3453 - val_loss: 0.3396
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3388
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3368
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3400
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3441 - val_loss: 0.3396
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3394
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3378
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3445 - val_loss: 0.3390
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3376
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3353
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3369
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3381
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3403
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3386
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3360
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3400
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3377
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3386
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3388
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3431 - val_loss: 0.3395
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3386
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3351
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3422 - val_loss: 0.3358
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3369
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3372
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3359
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3413 - val_loss: 0.3370
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3439 - val_loss: 0.3360
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3414
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3373
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3356
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3392
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3347
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3421 - val_loss: 0.3359
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3411 - val_loss: 0.3389
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3404 - val_loss: 0.3378
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3388
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3400 - val_loss: 0.3356
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3366
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3417 - val_loss: 0.3372
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3356
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3368
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3399 - val_loss: 0.3373
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3346
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3369
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3400 - val_loss: 0.3342
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3409 - val_loss: 0.3347
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3362
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3354
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3410 - val_loss: 0.3350
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3376
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3398 - val_loss: 0.3365
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3394 - val_loss: 0.3355
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3416 - val_loss: 0.3355
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3350
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3397 - val_loss: 0.3354
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3396 - val_loss: 0.3340
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_436 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1440 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1440 [()] 0 tf_op_layer_Shape_1440[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_144 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1440[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_144[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_432 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_144 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_432[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1441 (TensorF [(2,)] 0 tf_op_layer_Add_144[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1443 (TensorF [(2,)] 0 tf_op_layer_Add_144[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1442 (TensorF [(2,)] 0 tf_op_layer_Add_144[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1444 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1446 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1445 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1447 (TensorF [(2,)] 0 tf_op_layer_Add_144[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1449 (TensorF [(2,)] 0 tf_op_layer_Add_144[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1448 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1441 [()] 0 tf_op_layer_Shape_1441[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1443 [()] 0 tf_op_layer_Shape_1443[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1442 [()] 0 tf_op_layer_Shape_1442[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1444 [()] 0 tf_op_layer_Shape_1444[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1446 [()] 0 tf_op_layer_Shape_1446[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1445 [()] 0 tf_op_layer_Shape_1445[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1447 [()] 0 tf_op_layer_Shape_1447[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1449 [()] 0 tf_op_layer_Shape_1449[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1448 [()] 0 tf_op_layer_Shape_1448[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_864/shape ( [(3,)] 0 tf_op_layer_strided_slice_1441[0]
tf_op_layer_strided_slice_1443[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_865/shape ( [(3,)] 0 tf_op_layer_strided_slice_1442[0]
tf_op_layer_strided_slice_1443[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_866/shape ( [(3,)] 0 tf_op_layer_strided_slice_1444[0]
tf_op_layer_strided_slice_1446[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_867/shape ( [(3,)] 0 tf_op_layer_strided_slice_1445[0]
tf_op_layer_strided_slice_1446[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_868/shape ( [(3,)] 0 tf_op_layer_strided_slice_1447[0]
tf_op_layer_strided_slice_1449[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_869/shape ( [(3,)] 0 tf_op_layer_strided_slice_1448[0]
tf_op_layer_strided_slice_1449[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_864 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_144[0][0]
tf_op_layer_Reshape_864/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_864/multiples [(3,)] 0 tf_op_layer_strided_slice_1442[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_865 (Tensor [(1, None, None)] 0 tf_op_layer_Add_144[0][0]
tf_op_layer_Reshape_865/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_865/multiples [(3,)] 0 tf_op_layer_strided_slice_1441[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_866 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_866/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_866/multiples [(3,)] 0 tf_op_layer_strided_slice_1445[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_867 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_867/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_867/multiples [(3,)] 0 tf_op_layer_strided_slice_1444[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_868 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_144[0][0]
tf_op_layer_Reshape_868/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_868/multiples [(3,)] 0 tf_op_layer_strided_slice_1448[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_869 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_869/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_869/multiples [(3,)] 0 tf_op_layer_strided_slice_1447[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_864 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_864[0][0]
tf_op_layer_Tile_864/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_865 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_865[0][0]
tf_op_layer_Tile_865/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_866 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_866[0][0]
tf_op_layer_Tile_866/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_867 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_867[0][0]
tf_op_layer_Tile_867/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_868 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_868[0][0]
tf_op_layer_Tile_868/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_869 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_869[0][0]
tf_op_layer_Tile_869/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_720 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_864[0][0]
tf_op_layer_Tile_865[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_721 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_866[0][0]
tf_op_layer_Tile_867[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_722 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_868[0][0]
tf_op_layer_Tile_869[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_576 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_720[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_577 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_721[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_578 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_722[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1008 (TensorFl [(None, None)] 0 tf_op_layer_Square_576[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1009 (TensorFl [(None, None)] 0 tf_op_layer_Square_577[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1010 (TensorFl [(None, None)] 0 tf_op_layer_Square_578[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_432 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1008[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_432 (TensorFlo [()] 0 tf_op_layer_strided_slice_1443[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_433 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1009[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_433 (TensorFlo [()] 0 tf_op_layer_strided_slice_1446[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_434 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1010[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_434 (TensorFlo [()] 0 tf_op_layer_strided_slice_1449[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_432 (Tensor [(None, None)] 0 tf_op_layer_Neg_432[0][0]
tf_op_layer_Cast_432[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_433 (Tensor [(None, None)] 0 tf_op_layer_Neg_433[0][0]
tf_op_layer_Cast_433[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_434 (Tensor [(None, None)] 0 tf_op_layer_Neg_434[0][0]
tf_op_layer_Cast_434[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_432 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_432[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_433 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_433[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_434 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_434[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1011 (TensorFl [()] 0 tf_op_layer_Exp_432[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1012 (TensorFl [()] 0 tf_op_layer_Exp_433[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1013 (TensorFl [()] 0 tf_op_layer_Exp_434[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_724 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_288 (TensorFl [()] 0 tf_op_layer_Mean_1011[0][0]
tf_op_layer_Mean_1012[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_433 (TensorFlow [()] 0 tf_op_layer_Mean_1013[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_579 (TensorF [(None, 97)] 0 tf_op_layer_Sub_724[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_723 (TensorFlow [()] 0 tf_op_layer_AddV2_288[0][0]
tf_op_layer_Mul_433[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_144 (TensorFlow [(None,)] 0 tf_op_layer_Square_579[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_434 (TensorFlow [()] 0 tf_op_layer_Sub_723[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_289 (TensorFl [(None,)] 0 tf_op_layer_Sum_144[0][0]
tf_op_layer_Mul_434[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1014 (TensorFl [()] 0 tf_op_layer_AddV2_289[0][0]
__________________________________________________________________________________________________
add_loss_144 (AddLoss) () 0 tf_op_layer_Mean_1014[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 10s - loss: 8.6330WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0982s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9716 - val_loss: 0.9498
Epoch 2/100
218/218 [==============================] - 0s 892us/step - loss: 0.8421 - val_loss: 0.7472
Epoch 3/100
218/218 [==============================] - 0s 934us/step - loss: 0.7019 - val_loss: 0.6602
Epoch 4/100
218/218 [==============================] - 0s 934us/step - loss: 0.6425 - val_loss: 0.6090
Epoch 5/100
218/218 [==============================] - 0s 901us/step - loss: 0.6001 - val_loss: 0.5787
Epoch 6/100
218/218 [==============================] - 0s 918us/step - loss: 0.5855 - val_loss: 0.5664
Epoch 7/100
218/218 [==============================] - 0s 937us/step - loss: 0.5707 - val_loss: 0.5556
Epoch 8/100
218/218 [==============================] - 0s 903us/step - loss: 0.5576 - val_loss: 0.5364
Epoch 9/100
218/218 [==============================] - 0s 906us/step - loss: 0.5492 - val_loss: 0.5348
Epoch 10/100
218/218 [==============================] - 0s 898us/step - loss: 0.5407 - val_loss: 0.5232
Epoch 11/100
218/218 [==============================] - 0s 905us/step - loss: 0.5352 - val_loss: 0.5150
Epoch 12/100
218/218 [==============================] - 0s 904us/step - loss: 0.5282 - val_loss: 0.5133
Epoch 13/100
218/218 [==============================] - 0s 904us/step - loss: 0.5247 - val_loss: 0.5141
Epoch 14/100
218/218 [==============================] - 0s 905us/step - loss: 0.5184 - val_loss: 0.5059
Epoch 15/100
218/218 [==============================] - 0s 907us/step - loss: 0.5164 - val_loss: 0.5049
Epoch 16/100
218/218 [==============================] - 0s 903us/step - loss: 0.5156 - val_loss: 0.5011
Epoch 17/100
218/218 [==============================] - 0s 899us/step - loss: 0.5117 - val_loss: 0.4949
Epoch 18/100
218/218 [==============================] - 0s 922us/step - loss: 0.5078 - val_loss: 0.4940
Epoch 19/100
218/218 [==============================] - 0s 907us/step - loss: 0.5052 - val_loss: 0.4916
Epoch 20/100
218/218 [==============================] - 0s 910us/step - loss: 0.5057 - val_loss: 0.4870
Epoch 21/100
218/218 [==============================] - 0s 908us/step - loss: 0.4995 - val_loss: 0.4890
Epoch 22/100
218/218 [==============================] - 0s 901us/step - loss: 0.4988 - val_loss: 0.4852
Epoch 23/100
218/218 [==============================] - 0s 895us/step - loss: 0.4962 - val_loss: 0.4831
Epoch 24/100
218/218 [==============================] - 0s 902us/step - loss: 0.4944 - val_loss: 0.4851
Epoch 25/100
218/218 [==============================] - 0s 897us/step - loss: 0.4929 - val_loss: 0.4775
Epoch 26/100
218/218 [==============================] - 0s 900us/step - loss: 0.4937 - val_loss: 0.4781
Epoch 27/100
218/218 [==============================] - 0s 902us/step - loss: 0.4922 - val_loss: 0.4779
Epoch 28/100
218/218 [==============================] - 0s 902us/step - loss: 0.4932 - val_loss: 0.4810
Epoch 29/100
218/218 [==============================] - 0s 900us/step - loss: 0.4897 - val_loss: 0.4802
Epoch 30/100
218/218 [==============================] - 0s 901us/step - loss: 0.4874 - val_loss: 0.4764
Epoch 31/100
218/218 [==============================] - 0s 901us/step - loss: 0.4881 - val_loss: 0.4751
Epoch 32/100
218/218 [==============================] - 0s 901us/step - loss: 0.4856 - val_loss: 0.4752
Epoch 33/100
218/218 [==============================] - 0s 900us/step - loss: 0.4844 - val_loss: 0.4744
Epoch 34/100
218/218 [==============================] - 0s 909us/step - loss: 0.4869 - val_loss: 0.4698
Epoch 35/100
218/218 [==============================] - 0s 903us/step - loss: 0.4852 - val_loss: 0.4714
Epoch 36/100
218/218 [==============================] - 0s 900us/step - loss: 0.4839 - val_loss: 0.4654
Epoch 37/100
218/218 [==============================] - 0s 901us/step - loss: 0.4816 - val_loss: 0.4681
Epoch 38/100
218/218 [==============================] - 0s 897us/step - loss: 0.4804 - val_loss: 0.4712
Epoch 39/100
218/218 [==============================] - 0s 905us/step - loss: 0.4792 - val_loss: 0.4698
Epoch 40/100
218/218 [==============================] - 0s 902us/step - loss: 0.4812 - val_loss: 0.4716
Epoch 41/100
218/218 [==============================] - 0s 907us/step - loss: 0.4765 - val_loss: 0.4658
Epoch 42/100
218/218 [==============================] - 0s 901us/step - loss: 0.4805 - val_loss: 0.4663
Epoch 43/100
218/218 [==============================] - 0s 902us/step - loss: 0.4801 - val_loss: 0.4633
Epoch 44/100
218/218 [==============================] - 0s 925us/step - loss: 0.4775 - val_loss: 0.4629
Epoch 45/100
218/218 [==============================] - 0s 935us/step - loss: 0.4786 - val_loss: 0.4674
Epoch 46/100
218/218 [==============================] - 0s 902us/step - loss: 0.4775 - val_loss: 0.4596
Epoch 47/100
218/218 [==============================] - 0s 903us/step - loss: 0.4767 - val_loss: 0.4675
Epoch 48/100
218/218 [==============================] - 0s 956us/step - loss: 0.4768 - val_loss: 0.4611
Epoch 49/100
218/218 [==============================] - 0s 899us/step - loss: 0.4740 - val_loss: 0.4615
Epoch 50/100
218/218 [==============================] - 0s 901us/step - loss: 0.4753 - val_loss: 0.4599
Epoch 51/100
218/218 [==============================] - 0s 898us/step - loss: 0.4763 - val_loss: 0.4612
Epoch 52/100
218/218 [==============================] - 0s 899us/step - loss: 0.4765 - val_loss: 0.4582
Epoch 53/100
218/218 [==============================] - 0s 900us/step - loss: 0.4733 - val_loss: 0.4579
Epoch 54/100
218/218 [==============================] - 0s 892us/step - loss: 0.4731 - val_loss: 0.4638
Epoch 55/100
218/218 [==============================] - 0s 901us/step - loss: 0.4742 - val_loss: 0.4636
Epoch 56/100
218/218 [==============================] - 0s 905us/step - loss: 0.4715 - val_loss: 0.4579
Epoch 57/100
218/218 [==============================] - 0s 913us/step - loss: 0.4727 - val_loss: 0.4599
Epoch 58/100
218/218 [==============================] - 0s 897us/step - loss: 0.4742 - val_loss: 0.4566
Epoch 59/100
218/218 [==============================] - 0s 901us/step - loss: 0.4721 - val_loss: 0.4602
Epoch 60/100
218/218 [==============================] - 0s 897us/step - loss: 0.4709 - val_loss: 0.4562
Epoch 61/100
218/218 [==============================] - 0s 900us/step - loss: 0.4719 - val_loss: 0.4567
Epoch 62/100
218/218 [==============================] - 0s 898us/step - loss: 0.4721 - val_loss: 0.4570
Epoch 63/100
218/218 [==============================] - 0s 895us/step - loss: 0.4702 - val_loss: 0.4603
Epoch 64/100
218/218 [==============================] - 0s 898us/step - loss: 0.4721 - val_loss: 0.4581
Epoch 65/100
218/218 [==============================] - 0s 903us/step - loss: 0.4682 - val_loss: 0.4586
Epoch 66/100
218/218 [==============================] - 0s 896us/step - loss: 0.4696 - val_loss: 0.4537
Epoch 67/100
218/218 [==============================] - 0s 901us/step - loss: 0.4709 - val_loss: 0.4587
Epoch 68/100
218/218 [==============================] - 0s 895us/step - loss: 0.4694 - val_loss: 0.4542
Epoch 69/100
218/218 [==============================] - 0s 893us/step - loss: 0.4716 - val_loss: 0.4550
Epoch 70/100
218/218 [==============================] - 0s 898us/step - loss: 0.4692 - val_loss: 0.4518
Epoch 71/100
218/218 [==============================] - 0s 905us/step - loss: 0.4698 - val_loss: 0.4578
Epoch 72/100
218/218 [==============================] - 0s 907us/step - loss: 0.4685 - val_loss: 0.4518
Epoch 73/100
218/218 [==============================] - 0s 897us/step - loss: 0.4670 - val_loss: 0.4561
Epoch 74/100
218/218 [==============================] - 0s 905us/step - loss: 0.4678 - val_loss: 0.4520
Epoch 75/100
218/218 [==============================] - 0s 924us/step - loss: 0.4660 - val_loss: 0.4554
Epoch 76/100
218/218 [==============================] - 0s 940us/step - loss: 0.4669 - val_loss: 0.4531
Epoch 77/100
218/218 [==============================] - 0s 931us/step - loss: 0.4682 - val_loss: 0.4605
Epoch 78/100
218/218 [==============================] - 0s 926us/step - loss: 0.4646 - val_loss: 0.4569
Epoch 79/100
218/218 [==============================] - 0s 929us/step - loss: 0.4689 - val_loss: 0.4533
Epoch 80/100
218/218 [==============================] - 0s 930us/step - loss: 0.4677 - val_loss: 0.4587
Epoch 81/100
218/218 [==============================] - 0s 925us/step - loss: 0.4672 - val_loss: 0.4551
Epoch 82/100
218/218 [==============================] - 0s 947us/step - loss: 0.4681 - val_loss: 0.4545
Epoch 83/100
218/218 [==============================] - 0s 924us/step - loss: 0.4647 - val_loss: 0.4541
Epoch 84/100
218/218 [==============================] - 0s 926us/step - loss: 0.4670 - val_loss: 0.4486
Epoch 85/100
218/218 [==============================] - 0s 923us/step - loss: 0.4645 - val_loss: 0.4525
Epoch 86/100
218/218 [==============================] - 0s 924us/step - loss: 0.4659 - val_loss: 0.4516
Epoch 87/100
218/218 [==============================] - 0s 930us/step - loss: 0.4654 - val_loss: 0.4493
Epoch 88/100
218/218 [==============================] - 0s 930us/step - loss: 0.4656 - val_loss: 0.4559
Epoch 89/100
218/218 [==============================] - 0s 933us/step - loss: 0.4639 - val_loss: 0.4523
Epoch 90/100
218/218 [==============================] - 0s 930us/step - loss: 0.4657 - val_loss: 0.4511
Epoch 91/100
218/218 [==============================] - 0s 929us/step - loss: 0.4645 - val_loss: 0.4543
Epoch 92/100
218/218 [==============================] - 0s 935us/step - loss: 0.4642 - val_loss: 0.4519
Epoch 93/100
218/218 [==============================] - 0s 934us/step - loss: 0.4649 - val_loss: 0.4511
Epoch 94/100
218/218 [==============================] - 0s 926us/step - loss: 0.4657 - val_loss: 0.4424
Epoch 95/100
218/218 [==============================] - 0s 940us/step - loss: 0.4643 - val_loss: 0.4505
Epoch 96/100
218/218 [==============================] - 0s 932us/step - loss: 0.4627 - val_loss: 0.4478
Epoch 97/100
218/218 [==============================] - 0s 925us/step - loss: 0.4658 - val_loss: 0.4504
Epoch 98/100
218/218 [==============================] - 0s 923us/step - loss: 0.4642 - val_loss: 0.4489
Epoch 99/100
218/218 [==============================] - 0s 922us/step - loss: 0.4619 - val_loss: 0.4517
Epoch 100/100
218/218 [==============================] - 0s 928us/step - loss: 0.4644 - val_loss: 0.4512
Calculating PHATE...
Running PHATE on 12797 observations and 97 variables.
Calculating graph and diffusion operator...
Calculating KNN search...
Calculated KNN search in 13.16 seconds.
Calculating affinities...
Calculated affinities in 0.24 seconds.
Calculated graph and diffusion operator in 13.41 seconds.
Calculating landmark operator...
Calculating SVD...
Calculated SVD in 0.73 seconds.
Calculating KMeans...
Calculated KMeans in 15.57 seconds.
Calculated landmark operator in 17.76 seconds.
Calculating optimal t...
Automatically selected t = 38
Calculated optimal t in 2.20 seconds.
Calculating diffusion potential...
Calculated diffusion potential in 0.53 seconds.
Calculating metric MDS...
Calculated metric MDS in 16.97 seconds.
Calculated PHATE in 50.88 seconds.
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
dense_437 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_438 (Dense) (None, 32) 2080 dense_437[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_438[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_438[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 8,550
Trainable params: 8,550
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_439 (Dense) (None, 32) 128
_________________________________________________________________
dense_440 (Dense) (None, 64) 2112
_________________________________________________________________
dense_441 (Dense) (None, 97) 6305
=================================================================
Total params: 8,545
Trainable params: 8,545
Non-trainable params: 0
_________________________________________________________________
Model: "runs_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 8550 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 8545 encoder[0][2]
__________________________________________________________________________________________________
dense_437 (Dense) (None, 64) 6272 default_input[0][0]
__________________________________________________________________________________________________
dense_438 (Dense) (None, 32) 2080 dense_437[0][0]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 99 dense_438[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 99 dense_438[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1450 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1450 [()] 0 tf_op_layer_Shape_1450[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_145 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1450[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_145[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_435 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_145 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_435[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1451 (TensorF [(2,)] 0 tf_op_layer_Add_145[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1453 (TensorF [(2,)] 0 tf_op_layer_Add_145[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1452 (TensorF [(2,)] 0 tf_op_layer_Add_145[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1454 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1456 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1455 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1457 (TensorF [(2,)] 0 tf_op_layer_Add_145[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1459 (TensorF [(2,)] 0 tf_op_layer_Add_145[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1458 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1451 [()] 0 tf_op_layer_Shape_1451[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1453 [()] 0 tf_op_layer_Shape_1453[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1452 [()] 0 tf_op_layer_Shape_1452[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1454 [()] 0 tf_op_layer_Shape_1454[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1456 [()] 0 tf_op_layer_Shape_1456[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1455 [()] 0 tf_op_layer_Shape_1455[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1457 [()] 0 tf_op_layer_Shape_1457[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1459 [()] 0 tf_op_layer_Shape_1459[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1458 [()] 0 tf_op_layer_Shape_1458[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_870/shape ( [(3,)] 0 tf_op_layer_strided_slice_1451[0]
tf_op_layer_strided_slice_1453[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_871/shape ( [(3,)] 0 tf_op_layer_strided_slice_1452[0]
tf_op_layer_strided_slice_1453[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_872/shape ( [(3,)] 0 tf_op_layer_strided_slice_1454[0]
tf_op_layer_strided_slice_1456[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_873/shape ( [(3,)] 0 tf_op_layer_strided_slice_1455[0]
tf_op_layer_strided_slice_1456[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_874/shape ( [(3,)] 0 tf_op_layer_strided_slice_1457[0]
tf_op_layer_strided_slice_1459[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_875/shape ( [(3,)] 0 tf_op_layer_strided_slice_1458[0]
tf_op_layer_strided_slice_1459[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_870 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_145[0][0]
tf_op_layer_Reshape_870/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_870/multiples [(3,)] 0 tf_op_layer_strided_slice_1452[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_871 (Tensor [(1, None, None)] 0 tf_op_layer_Add_145[0][0]
tf_op_layer_Reshape_871/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_871/multiples [(3,)] 0 tf_op_layer_strided_slice_1451[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_872 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_872/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_872/multiples [(3,)] 0 tf_op_layer_strided_slice_1455[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_873 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_873/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_873/multiples [(3,)] 0 tf_op_layer_strided_slice_1454[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_874 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_145[0][0]
tf_op_layer_Reshape_874/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_874/multiples [(3,)] 0 tf_op_layer_strided_slice_1458[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_875 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_875/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_875/multiples [(3,)] 0 tf_op_layer_strided_slice_1457[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_870 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_870[0][0]
tf_op_layer_Tile_870/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_871 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_871[0][0]
tf_op_layer_Tile_871/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_872 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_872[0][0]
tf_op_layer_Tile_872/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_873 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_873[0][0]
tf_op_layer_Tile_873/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_874 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_874[0][0]
tf_op_layer_Tile_874/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_875 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_875[0][0]
tf_op_layer_Tile_875/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_725 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_870[0][0]
tf_op_layer_Tile_871[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_726 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_872[0][0]
tf_op_layer_Tile_873[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_727 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_874[0][0]
tf_op_layer_Tile_875[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_580 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_725[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_581 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_726[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_582 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_727[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1015 (TensorFl [(None, None)] 0 tf_op_layer_Square_580[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1016 (TensorFl [(None, None)] 0 tf_op_layer_Square_581[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1017 (TensorFl [(None, None)] 0 tf_op_layer_Square_582[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_435 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1015[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_435 (TensorFlo [()] 0 tf_op_layer_strided_slice_1453[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_436 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1016[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_436 (TensorFlo [()] 0 tf_op_layer_strided_slice_1456[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_437 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1017[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_437 (TensorFlo [()] 0 tf_op_layer_strided_slice_1459[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_435 (Tensor [(None, None)] 0 tf_op_layer_Neg_435[0][0]
tf_op_layer_Cast_435[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_436 (Tensor [(None, None)] 0 tf_op_layer_Neg_436[0][0]
tf_op_layer_Cast_436[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_437 (Tensor [(None, None)] 0 tf_op_layer_Neg_437[0][0]
tf_op_layer_Cast_437[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_435 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_435[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_436 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_436[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_437 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_437[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1018 (TensorFl [()] 0 tf_op_layer_Exp_435[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1019 (TensorFl [()] 0 tf_op_layer_Exp_436[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1020 (TensorFl [()] 0 tf_op_layer_Exp_437[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_729 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_290 (TensorFl [()] 0 tf_op_layer_Mean_1018[0][0]
tf_op_layer_Mean_1019[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_436 (TensorFlow [()] 0 tf_op_layer_Mean_1020[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_583 (TensorF [(None, 97)] 0 tf_op_layer_Sub_729[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_728 (TensorFlow [()] 0 tf_op_layer_AddV2_290[0][0]
tf_op_layer_Mul_436[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_145 (TensorFlow [(None,)] 0 tf_op_layer_Square_583[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_437 (TensorFlow [()] 0 tf_op_layer_Sub_728[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_291 (TensorFl [(None,)] 0 tf_op_layer_Sum_145[0][0]
tf_op_layer_Mul_437[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1021 (TensorFl [()] 0 tf_op_layer_AddV2_291[0][0]
__________________________________________________________________________________________________
add_loss_145 (AddLoss) () 0 tf_op_layer_Mean_1021[0][0]
==================================================================================================
Total params: 17,095
Trainable params: 17,095
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 12s - loss: 7.8086WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0012s vs `on_train_batch_end` time: 0.1153s). Check your callbacks.
218/218 [==============================] - 1s 2ms/step - loss: 0.9648 - val_loss: 0.4974
Epoch 2/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4688 - val_loss: 0.4176
Epoch 3/100
218/218 [==============================] - 0s 1ms/step - loss: 0.4101 - val_loss: 0.3817
Epoch 4/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3886 - val_loss: 0.3714
Epoch 5/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3809 - val_loss: 0.3683
Epoch 6/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3759 - val_loss: 0.3597
Epoch 7/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3730 - val_loss: 0.3578
Epoch 8/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3700 - val_loss: 0.3557
Epoch 9/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3641 - val_loss: 0.3561
Epoch 10/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3644 - val_loss: 0.3513
Epoch 11/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3633 - val_loss: 0.3491
Epoch 12/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3605 - val_loss: 0.3505
Epoch 13/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3597 - val_loss: 0.3495
Epoch 14/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3588 - val_loss: 0.3521
Epoch 15/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3583 - val_loss: 0.3475
Epoch 16/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3560 - val_loss: 0.3440
Epoch 17/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3570 - val_loss: 0.3463
Epoch 18/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3562 - val_loss: 0.3438
Epoch 19/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3535 - val_loss: 0.3429
Epoch 20/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3541 - val_loss: 0.3454
Epoch 21/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3534 - val_loss: 0.3411
Epoch 22/100
218/218 [==============================] - 0s 987us/step - loss: 0.3530 - val_loss: 0.3425
Epoch 23/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3542 - val_loss: 0.3418
Epoch 24/100
218/218 [==============================] - 0s 990us/step - loss: 0.3519 - val_loss: 0.3433
Epoch 25/100
218/218 [==============================] - 0s 991us/step - loss: 0.3526 - val_loss: 0.3407
Epoch 26/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3519 - val_loss: 0.3401
Epoch 27/100
218/218 [==============================] - 0s 988us/step - loss: 0.3496 - val_loss: 0.3404
Epoch 28/100
218/218 [==============================] - 0s 984us/step - loss: 0.3510 - val_loss: 0.3419
Epoch 29/100
218/218 [==============================] - 0s 985us/step - loss: 0.3509 - val_loss: 0.3399
Epoch 30/100
218/218 [==============================] - 0s 981us/step - loss: 0.3501 - val_loss: 0.3415
Epoch 31/100
218/218 [==============================] - 0s 978us/step - loss: 0.3490 - val_loss: 0.3400
Epoch 32/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3499 - val_loss: 0.3412
Epoch 33/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3493 - val_loss: 0.3369
Epoch 34/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3486 - val_loss: 0.3377
Epoch 35/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3424
Epoch 36/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3395
Epoch 37/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3489 - val_loss: 0.3421
Epoch 38/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3488 - val_loss: 0.3416
Epoch 39/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3486 - val_loss: 0.3381
Epoch 40/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3475 - val_loss: 0.3382
Epoch 41/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3494 - val_loss: 0.3365
Epoch 42/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3480 - val_loss: 0.3414
Epoch 43/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3484 - val_loss: 0.3399
Epoch 44/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3457 - val_loss: 0.3377
Epoch 45/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3462 - val_loss: 0.3384
Epoch 46/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3465 - val_loss: 0.3366
Epoch 47/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3467 - val_loss: 0.3404
Epoch 48/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3470 - val_loss: 0.3367
Epoch 49/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3446 - val_loss: 0.3381
Epoch 50/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3469 - val_loss: 0.3385
Epoch 51/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3368
Epoch 52/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3468 - val_loss: 0.3369
Epoch 53/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3463 - val_loss: 0.3421
Epoch 54/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3456 - val_loss: 0.3377
Epoch 55/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3459 - val_loss: 0.3359
Epoch 56/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3444 - val_loss: 0.3381
Epoch 57/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3358
Epoch 58/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3357
Epoch 59/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3449 - val_loss: 0.3363
Epoch 60/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3450 - val_loss: 0.3410
Epoch 61/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3447 - val_loss: 0.3355
Epoch 62/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3448 - val_loss: 0.3369
Epoch 63/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3438 - val_loss: 0.3359
Epoch 64/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3366
Epoch 65/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3433 - val_loss: 0.3353
Epoch 66/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3430 - val_loss: 0.3348
Epoch 67/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3434 - val_loss: 0.3385
Epoch 68/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3437 - val_loss: 0.3369
Epoch 69/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3366
Epoch 70/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3435 - val_loss: 0.3367
Epoch 71/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3428 - val_loss: 0.3362
Epoch 72/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3419 - val_loss: 0.3368
Epoch 73/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3429 - val_loss: 0.3344
Epoch 74/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3362
Epoch 75/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3436 - val_loss: 0.3339
Epoch 76/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3443 - val_loss: 0.3355
Epoch 77/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3425 - val_loss: 0.3333
Epoch 78/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3408 - val_loss: 0.3367
Epoch 79/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3442 - val_loss: 0.3359
Epoch 80/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3375
Epoch 81/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3331
Epoch 82/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3360
Epoch 83/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3412 - val_loss: 0.3339
Epoch 84/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3423 - val_loss: 0.3370
Epoch 85/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3432 - val_loss: 0.3358
Epoch 86/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3323
Epoch 87/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3343
Epoch 88/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3418 - val_loss: 0.3325
Epoch 89/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3427 - val_loss: 0.3328
Epoch 90/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3424 - val_loss: 0.3343
Epoch 91/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3426 - val_loss: 0.3356
Epoch 92/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3420 - val_loss: 0.3338
Epoch 93/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3414 - val_loss: 0.3323
Epoch 94/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3327
Epoch 95/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3415 - val_loss: 0.3330
Epoch 96/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3400 - val_loss: 0.3367
Epoch 97/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3401 - val_loss: 0.3334
Epoch 98/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3320
Epoch 99/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3407 - val_loss: 0.3317
Epoch 100/100
218/218 [==============================] - 0s 1ms/step - loss: 0.3403 - val_loss: 0.3340
None
Model: "encoder"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
==================================================================================================
Total params: 588
Trainable params: 588
Non-trainable params: 0
__________________________________________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
z_sampling (InputLayer) [(None, 3)] 0
_________________________________________________________________
dense_442 (Dense) (None, 97) 388
=================================================================
Total params: 388
Trainable params: 388
Non-trainable params: 0
_________________________________________________________________
Model: "vae_3_scivae"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
default_input (InputLayer) [(None, 97)] 0
__________________________________________________________________________________________________
encoder (Functional) [(None, 3), (None, 3 588 default_input[0][0]
__________________________________________________________________________________________________
decoder (Functional) (None, 97) 388 encoder[0][2]
__________________________________________________________________________________________________
z_mean (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z_log_sigma (Dense) (None, 3) 294 default_input[0][0]
__________________________________________________________________________________________________
z (Lambda) (None, 3) 0 z_mean[0][0]
z_log_sigma[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1460 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1460 [()] 0 tf_op_layer_Shape_1460[0][0]
__________________________________________________________________________________________________
tf_op_layer_Pack_146 (TensorFlo [(2,)] 0 tf_op_layer_strided_slice_1460[0]
__________________________________________________________________________________________________
tf_op_layer_RandomStandardNorma [(None, 3)] 0 tf_op_layer_Pack_146[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_438 (TensorFlow [(None, 3)] 0 tf_op_layer_RandomStandardNormal_
__________________________________________________________________________________________________
tf_op_layer_Add_146 (TensorFlow [(None, 3)] 0 tf_op_layer_Mul_438[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1461 (TensorF [(2,)] 0 tf_op_layer_Add_146[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1463 (TensorF [(2,)] 0 tf_op_layer_Add_146[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1462 (TensorF [(2,)] 0 tf_op_layer_Add_146[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1464 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1466 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1465 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1467 (TensorF [(2,)] 0 tf_op_layer_Add_146[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1469 (TensorF [(2,)] 0 tf_op_layer_Add_146[0][0]
__________________________________________________________________________________________________
tf_op_layer_Shape_1468 (TensorF [(2,)] 0 z[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1461 [()] 0 tf_op_layer_Shape_1461[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1463 [()] 0 tf_op_layer_Shape_1463[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1462 [()] 0 tf_op_layer_Shape_1462[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1464 [()] 0 tf_op_layer_Shape_1464[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1466 [()] 0 tf_op_layer_Shape_1466[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1465 [()] 0 tf_op_layer_Shape_1465[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1467 [()] 0 tf_op_layer_Shape_1467[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1469 [()] 0 tf_op_layer_Shape_1469[0][0]
__________________________________________________________________________________________________
tf_op_layer_strided_slice_1468 [()] 0 tf_op_layer_Shape_1468[0][0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_876/shape ( [(3,)] 0 tf_op_layer_strided_slice_1461[0]
tf_op_layer_strided_slice_1463[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_877/shape ( [(3,)] 0 tf_op_layer_strided_slice_1462[0]
tf_op_layer_strided_slice_1463[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_878/shape ( [(3,)] 0 tf_op_layer_strided_slice_1464[0]
tf_op_layer_strided_slice_1466[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_879/shape ( [(3,)] 0 tf_op_layer_strided_slice_1465[0]
tf_op_layer_strided_slice_1466[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_880/shape ( [(3,)] 0 tf_op_layer_strided_slice_1467[0]
tf_op_layer_strided_slice_1469[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_881/shape ( [(3,)] 0 tf_op_layer_strided_slice_1468[0]
tf_op_layer_strided_slice_1469[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_876 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_146[0][0]
tf_op_layer_Reshape_876/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_876/multiples [(3,)] 0 tf_op_layer_strided_slice_1462[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_877 (Tensor [(1, None, None)] 0 tf_op_layer_Add_146[0][0]
tf_op_layer_Reshape_877/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_877/multiples [(3,)] 0 tf_op_layer_strided_slice_1461[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_878 (Tensor [(None, 1, None)] 0 z[0][0]
tf_op_layer_Reshape_878/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_878/multiples [(3,)] 0 tf_op_layer_strided_slice_1465[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_879 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_879/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_879/multiples [(3,)] 0 tf_op_layer_strided_slice_1464[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_880 (Tensor [(None, 1, None)] 0 tf_op_layer_Add_146[0][0]
tf_op_layer_Reshape_880/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_880/multiples [(3,)] 0 tf_op_layer_strided_slice_1468[0]
__________________________________________________________________________________________________
tf_op_layer_Reshape_881 (Tensor [(1, None, None)] 0 z[0][0]
tf_op_layer_Reshape_881/shape[0][
__________________________________________________________________________________________________
tf_op_layer_Tile_881/multiples [(3,)] 0 tf_op_layer_strided_slice_1467[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_876 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_876[0][0]
tf_op_layer_Tile_876/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_877 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_877[0][0]
tf_op_layer_Tile_877/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_878 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_878[0][0]
tf_op_layer_Tile_878/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_879 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_879[0][0]
tf_op_layer_Tile_879/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_880 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_880[0][0]
tf_op_layer_Tile_880/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Tile_881 (TensorFlo [(None, None, None)] 0 tf_op_layer_Reshape_881[0][0]
tf_op_layer_Tile_881/multiples[0]
__________________________________________________________________________________________________
tf_op_layer_Sub_730 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_876[0][0]
tf_op_layer_Tile_877[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_731 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_878[0][0]
tf_op_layer_Tile_879[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_732 (TensorFlow [(None, None, None)] 0 tf_op_layer_Tile_880[0][0]
tf_op_layer_Tile_881[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_584 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_730[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_585 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_731[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_586 (TensorF [(None, None, None)] 0 tf_op_layer_Sub_732[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1022 (TensorFl [(None, None)] 0 tf_op_layer_Square_584[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1023 (TensorFl [(None, None)] 0 tf_op_layer_Square_585[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1024 (TensorFl [(None, None)] 0 tf_op_layer_Square_586[0][0]
__________________________________________________________________________________________________
tf_op_layer_Neg_438 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1022[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_438 (TensorFlo [()] 0 tf_op_layer_strided_slice_1463[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_439 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1023[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_439 (TensorFlo [()] 0 tf_op_layer_strided_slice_1466[0]
__________________________________________________________________________________________________
tf_op_layer_Neg_440 (TensorFlow [(None, None)] 0 tf_op_layer_Mean_1024[0][0]
__________________________________________________________________________________________________
tf_op_layer_Cast_440 (TensorFlo [()] 0 tf_op_layer_strided_slice_1469[0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_438 (Tensor [(None, None)] 0 tf_op_layer_Neg_438[0][0]
tf_op_layer_Cast_438[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_439 (Tensor [(None, None)] 0 tf_op_layer_Neg_439[0][0]
tf_op_layer_Cast_439[0][0]
__________________________________________________________________________________________________
tf_op_layer_RealDiv_440 (Tensor [(None, None)] 0 tf_op_layer_Neg_440[0][0]
tf_op_layer_Cast_440[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_438 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_438[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_439 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_439[0][0]
__________________________________________________________________________________________________
tf_op_layer_Exp_440 (TensorFlow [(None, None)] 0 tf_op_layer_RealDiv_440[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1025 (TensorFl [()] 0 tf_op_layer_Exp_438[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1026 (TensorFl [()] 0 tf_op_layer_Exp_439[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1027 (TensorFl [()] 0 tf_op_layer_Exp_440[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_734 (TensorFlow [(None, 97)] 0 default_input[0][0]
decoder[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_292 (TensorFl [()] 0 tf_op_layer_Mean_1025[0][0]
tf_op_layer_Mean_1026[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_439 (TensorFlow [()] 0 tf_op_layer_Mean_1027[0][0]
__________________________________________________________________________________________________
tf_op_layer_Square_587 (TensorF [(None, 97)] 0 tf_op_layer_Sub_734[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sub_733 (TensorFlow [()] 0 tf_op_layer_AddV2_292[0][0]
tf_op_layer_Mul_439[0][0]
__________________________________________________________________________________________________
tf_op_layer_Sum_146 (TensorFlow [(None,)] 0 tf_op_layer_Square_587[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mul_440 (TensorFlow [()] 0 tf_op_layer_Sub_733[0][0]
__________________________________________________________________________________________________
tf_op_layer_AddV2_293 (TensorFl [(None,)] 0 tf_op_layer_Sum_146[0][0]
tf_op_layer_Mul_440[0][0]
__________________________________________________________________________________________________
tf_op_layer_Mean_1028 (TensorFl [()] 0 tf_op_layer_AddV2_293[0][0]
__________________________________________________________________________________________________
add_loss_146 (AddLoss) () 0 tf_op_layer_Mean_1028[0][0]
==================================================================================================
Total params: 976
Trainable params: 976
Non-trainable params: 0
__________________________________________________________________________________________________
None
Epoch 1/100
2/218 [..............................] - ETA: 9s - loss: 8.2040WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0011s vs `on_train_batch_end` time: 0.0864s). Check your callbacks.
218/218 [==============================] - 0s 2ms/step - loss: 1.9030 - val_loss: 0.9984
Epoch 2/100
218/218 [==============================] - 0s 880us/step - loss: 0.8815 - val_loss: 0.8272
Epoch 3/100
218/218 [==============================] - 0s 881us/step - loss: 0.7631 - val_loss: 0.7453
Epoch 4/100
218/218 [==============================] - 0s 890us/step - loss: 0.6902 - val_loss: 0.6814
Epoch 5/100
218/218 [==============================] - 0s 887us/step - loss: 0.6347 - val_loss: 0.6316
Epoch 6/100
218/218 [==============================] - 0s 887us/step - loss: 0.6048 - val_loss: 0.6053
Epoch 7/100
218/218 [==============================] - 0s 888us/step - loss: 0.5806 - val_loss: 0.6028
Epoch 8/100
218/218 [==============================] - 0s 885us/step - loss: 0.5704 - val_loss: 0.5750
Epoch 9/100
218/218 [==============================] - 0s 885us/step - loss: 0.5545 - val_loss: 0.5736
Epoch 10/100
218/218 [==============================] - 0s 888us/step - loss: 0.5456 - val_loss: 0.5670
Epoch 11/100
218/218 [==============================] - 0s 887us/step - loss: 0.5379 - val_loss: 0.5542
Epoch 12/100
218/218 [==============================] - 0s 880us/step - loss: 0.5341 - val_loss: 0.5559
Epoch 13/100
218/218 [==============================] - 0s 879us/step - loss: 0.5295 - val_loss: 0.5393
Epoch 14/100
218/218 [==============================] - 0s 883us/step - loss: 0.5266 - val_loss: 0.5372
Epoch 15/100
218/218 [==============================] - 0s 885us/step - loss: 0.5211 - val_loss: 0.5322
Epoch 16/100
218/218 [==============================] - 0s 890us/step - loss: 0.5147 - val_loss: 0.5398
Epoch 17/100
218/218 [==============================] - 0s 885us/step - loss: 0.5113 - val_loss: 0.5244
Epoch 18/100
218/218 [==============================] - 0s 887us/step - loss: 0.5084 - val_loss: 0.5313
Epoch 19/100
218/218 [==============================] - 0s 890us/step - loss: 0.5068 - val_loss: 0.5257
Epoch 20/100
218/218 [==============================] - 0s 892us/step - loss: 0.5041 - val_loss: 0.5228
Epoch 21/100
218/218 [==============================] - 0s 893us/step - loss: 0.5014 - val_loss: 0.5168
Epoch 22/100
218/218 [==============================] - 0s 891us/step - loss: 0.5002 - val_loss: 0.5150
Epoch 23/100
218/218 [==============================] - 0s 893us/step - loss: 0.4982 - val_loss: 0.5163
Epoch 24/100
218/218 [==============================] - 0s 892us/step - loss: 0.4975 - val_loss: 0.5215
Epoch 25/100
218/218 [==============================] - 0s 891us/step - loss: 0.4965 - val_loss: 0.5169
Epoch 26/100
218/218 [==============================] - 0s 887us/step - loss: 0.4929 - val_loss: 0.5131
Epoch 27/100
218/218 [==============================] - 0s 888us/step - loss: 0.4912 - val_loss: 0.5115
Epoch 28/100
218/218 [==============================] - 0s 890us/step - loss: 0.4909 - val_loss: 0.5101
Epoch 29/100
218/218 [==============================] - 0s 899us/step - loss: 0.4877 - val_loss: 0.5109
Epoch 30/100
218/218 [==============================] - 0s 897us/step - loss: 0.4884 - val_loss: 0.5066
Epoch 31/100
218/218 [==============================] - 0s 889us/step - loss: 0.4865 - val_loss: 0.5034
Epoch 32/100
218/218 [==============================] - 0s 892us/step - loss: 0.4864 - val_loss: 0.5049
Epoch 33/100
218/218 [==============================] - 0s 890us/step - loss: 0.4868 - val_loss: 0.5004
Epoch 34/100
218/218 [==============================] - 0s 893us/step - loss: 0.4834 - val_loss: 0.5004
Epoch 35/100
218/218 [==============================] - 0s 885us/step - loss: 0.4831 - val_loss: 0.5044
Epoch 36/100
218/218 [==============================] - 0s 893us/step - loss: 0.4815 - val_loss: 0.5048
Epoch 37/100
218/218 [==============================] - 0s 887us/step - loss: 0.4835 - val_loss: 0.4964
Epoch 38/100
218/218 [==============================] - 0s 894us/step - loss: 0.4809 - val_loss: 0.4993
Epoch 39/100
218/218 [==============================] - 0s 892us/step - loss: 0.4799 - val_loss: 0.4958
Epoch 40/100
218/218 [==============================] - 0s 886us/step - loss: 0.4791 - val_loss: 0.4995
Epoch 41/100
218/218 [==============================] - 0s 889us/step - loss: 0.4781 - val_loss: 0.4959
Epoch 42/100
218/218 [==============================] - 0s 893us/step - loss: 0.4767 - val_loss: 0.4928
Epoch 43/100
218/218 [==============================] - 0s 890us/step - loss: 0.4768 - val_loss: 0.4991
Epoch 44/100
218/218 [==============================] - 0s 889us/step - loss: 0.4789 - val_loss: 0.4952
Epoch 45/100
218/218 [==============================] - 0s 889us/step - loss: 0.4775 - val_loss: 0.4970
Epoch 46/100
218/218 [==============================] - 0s 889us/step - loss: 0.4754 - val_loss: 0.4952
Epoch 47/100
218/218 [==============================] - 0s 896us/step - loss: 0.4747 - val_loss: 0.4956
Epoch 48/100
218/218 [==============================] - 0s 892us/step - loss: 0.4752 - val_loss: 0.4970
Epoch 49/100
218/218 [==============================] - 0s 894us/step - loss: 0.4757 - val_loss: 0.4948
Epoch 50/100
218/218 [==============================] - 0s 887us/step - loss: 0.4743 - val_loss: 0.4906
Epoch 51/100
218/218 [==============================] - 0s 890us/step - loss: 0.4720 - val_loss: 0.4943
Epoch 52/100
218/218 [==============================] - 0s 896us/step - loss: 0.4727 - val_loss: 0.4910
Epoch 53/100
218/218 [==============================] - 0s 892us/step - loss: 0.4718 - val_loss: 0.4965
Epoch 54/100
218/218 [==============================] - 0s 904us/step - loss: 0.4728 - val_loss: 0.4903
Epoch 55/100
218/218 [==============================] - 0s 893us/step - loss: 0.4691 - val_loss: 0.4922
Epoch 56/100
218/218 [==============================] - 0s 890us/step - loss: 0.4706 - val_loss: 0.4879
Epoch 57/100
218/218 [==============================] - 0s 893us/step - loss: 0.4714 - val_loss: 0.4893
Epoch 58/100
218/218 [==============================] - 0s 896us/step - loss: 0.4716 - val_loss: 0.4873
Epoch 59/100
218/218 [==============================] - 0s 900us/step - loss: 0.4705 - val_loss: 0.4889
Epoch 60/100
218/218 [==============================] - 0s 892us/step - loss: 0.4710 - val_loss: 0.4903
Epoch 61/100
218/218 [==============================] - 0s 888us/step - loss: 0.4687 - val_loss: 0.4874
Epoch 62/100
218/218 [==============================] - 0s 890us/step - loss: 0.4681 - val_loss: 0.4852
Epoch 63/100
218/218 [==============================] - 0s 893us/step - loss: 0.4687 - val_loss: 0.4931
Epoch 64/100
218/218 [==============================] - 0s 904us/step - loss: 0.4697 - val_loss: 0.4892
Epoch 65/100
218/218 [==============================] - 0s 888us/step - loss: 0.4698 - val_loss: 0.4847
Epoch 66/100
218/218 [==============================] - 0s 890us/step - loss: 0.4669 - val_loss: 0.4869
Epoch 67/100
218/218 [==============================] - 0s 897us/step - loss: 0.4676 - val_loss: 0.4848
Epoch 68/100
218/218 [==============================] - 0s 888us/step - loss: 0.4674 - val_loss: 0.4840
Epoch 69/100
218/218 [==============================] - 0s 888us/step - loss: 0.4666 - val_loss: 0.4928
Epoch 70/100
218/218 [==============================] - 0s 887us/step - loss: 0.4662 - val_loss: 0.4866
Epoch 71/100
218/218 [==============================] - 0s 901us/step - loss: 0.4662 - val_loss: 0.4869
Epoch 72/100
218/218 [==============================] - 0s 896us/step - loss: 0.4630 - val_loss: 0.4850
Epoch 73/100
218/218 [==============================] - 0s 893us/step - loss: 0.4670 - val_loss: 0.4903
Epoch 74/100
218/218 [==============================] - 0s 892us/step - loss: 0.4661 - val_loss: 0.4839
Epoch 75/100
218/218 [==============================] - 0s 895us/step - loss: 0.4657 - val_loss: 0.4841
Epoch 76/100
218/218 [==============================] - 0s 891us/step - loss: 0.4649 - val_loss: 0.4845
Epoch 77/100
218/218 [==============================] - 0s 895us/step - loss: 0.4649 - val_loss: 0.4827
Epoch 78/100
218/218 [==============================] - 0s 893us/step - loss: 0.4637 - val_loss: 0.4859
Epoch 79/100
218/218 [==============================] - 0s 894us/step - loss: 0.4651 - val_loss: 0.4851
Epoch 80/100
218/218 [==============================] - 0s 909us/step - loss: 0.4629 - val_loss: 0.4853
Epoch 81/100
218/218 [==============================] - 0s 896us/step - loss: 0.4660 - val_loss: 0.4854
Epoch 82/100
218/218 [==============================] - 0s 897us/step - loss: 0.4646 - val_loss: 0.4845
Epoch 83/100
218/218 [==============================] - 0s 887us/step - loss: 0.4649 - val_loss: 0.4860
Epoch 84/100
218/218 [==============================] - 0s 895us/step - loss: 0.4639 - val_loss: 0.4761
Epoch 85/100
218/218 [==============================] - 0s 892us/step - loss: 0.4629 - val_loss: 0.4839
Epoch 86/100
218/218 [==============================] - 0s 895us/step - loss: 0.4636 - val_loss: 0.4826
Epoch 87/100
218/218 [==============================] - 0s 899us/step - loss: 0.4622 - val_loss: 0.4818
Epoch 88/100
218/218 [==============================] - 0s 891us/step - loss: 0.4627 - val_loss: 0.4816
Epoch 89/100
218/218 [==============================] - 0s 891us/step - loss: 0.4617 - val_loss: 0.4807
Epoch 90/100
218/218 [==============================] - 0s 891us/step - loss: 0.4619 - val_loss: 0.4847
Epoch 91/100
218/218 [==============================] - 0s 891us/step - loss: 0.4629 - val_loss: 0.4802
Epoch 92/100
218/218 [==============================] - 0s 894us/step - loss: 0.4631 - val_loss: 0.4799
Epoch 93/100
218/218 [==============================] - 0s 890us/step - loss: 0.4622 - val_loss: 0.4868
Epoch 94/100
218/218 [==============================] - 0s 905us/step - loss: 0.4627 - val_loss: 0.4768
Epoch 95/100
218/218 [==============================] - 0s 918us/step - loss: 0.4640 - val_loss: 0.4801
Epoch 96/100
218/218 [==============================] - 0s 918us/step - loss: 0.4589 - val_loss: 0.4772
Epoch 97/100
218/218 [==============================] - 0s 923us/step - loss: 0.4608 - val_loss: 0.4798
Epoch 98/100
218/218 [==============================] - 0s 923us/step - loss: 0.4606 - val_loss: 0.4776
Epoch 99/100
218/218 [==============================] - 0s 922us/step - loss: 0.4609 - val_loss: 0.4815
Epoch 100/100
218/218 [==============================] - 0s 916us/step - loss: 0.4614 - val_loss: 0.4728
fb_genes = ['Emx1', 'Eomes', 'Tbr1', 'Foxg1', 'Lhx6', 'Arx', 'Dlx1', 'Dlx2', 'Dlx5', 'Nr2e2', 'Otx2']
mb_genes = ['En1', 'En2', 'Lmx1a', 'Bhlhe23', 'Sall4']
hb_genes = ['Hoxb1', 'Krox20', 'Fev', 'Hoxd3', 'Phox2b']
sc_genes = ['Hoxd8', 'Hoxd9', 'Hoxd10', 'Hoxd11', 'Hoxd12', 'Hoxd13', 'Hoxa7', 'Hoxa9', 'Hoxa10', 'Hoxa11', 'Hoxa13',
'Hoxb9', 'Hoxb13', 'Hoxc8', 'Hoxc9', 'Hoxc10', 'Hoxc11', 'Hoxc12', 'Hoxc13']
ns_glutamatergic = ['Slc17a6', 'VGLUT2', 'Slc17a7', 'VGLUT1', 'Slc17a8', 'VGLUT3', 'Slc1a1', 'Slc1a2', 'Slc1a6']
ns_progenitors = ['Sox2', 'Hes1', 'Hes5', 'Vim']
prolif = ['Ccna1', 'Ccna2', 'Ccnd1', 'Ccnd2', 'Ccnd3', 'Ccne1', 'Ccne2', 'Cdc25a',
'Cdc25b', 'Cdc25c', 'E2f1', 'E2f2', 'E2f3', 'Mcm10', 'Mcm5', 'Mcm3', 'Mcm2', 'Cip2a']
def get_idxs(genes, g_id):
grp1_idxs = []
i = 0
for g in df[g_id].values:
if g in genes:
grp1_idxs.append(i)
i += 1
return grp1_idxs
from scipy import stats
def compute_dists(grp1_idxs, grp2_idxs, data):
centroid_grp1 = np.mean(data[grp1_idxs, :], axis=0)
grp_1_within_dist = np.sqrt(np.sum((data[grp1_idxs, :] - centroid_grp1) **2, axis=1))
centroid_grp2 = np.mean(data[grp2_idxs, :], axis=0)
grp_2_within_dist = np.sqrt(np.sum((data[grp2_idxs, :] - centroid_grp2) **2, axis=1))
all_idxs = grp1_idxs + grp2_idxs
centroid = np.mean(data[all_idxs, :], axis=0)
between_dist = np.sqrt(np.sum((data[all_idxs, :] - centroid) **2, axis=1))
t1_stat, t1_pval = stats.ttest_ind(grp_1_within_dist, between_dist)
t2_stat, t2_pval = stats.ttest_ind(grp_2_within_dist, between_dist)
# If both t1 and t2 are sig return that it was a success
if t1_pval < 0.05 and t2_pval < 0.05:
return [1, t1_pval, t2_pval, t1_stat, t2_stat]
else:
return [0, t1_pval, t2_pval, t1_stat, t2_stat]
def get_res(grp1, grp2, data, cond_label, data_label):
tog = compute_dists(grp1, grp2, data)
results_tog[cond_label][data_label] += tog[0]
results_t[cond_label][data_label] += (abs(tog[3]) + abs(tog[4])) / 2
# Compute it independently for each dimension
x = 0
for d in range(0, 3):
res = compute_dists(grp1, grp2, data[:, d:d+1])
x += res[0]
# Add 1 if at least 1 dim was sig.
results_indep[cond_label][data_label] += 1 if x > 0 else 0
results_indep = {}
results_tog = {}
results_t = {}
for c in comp:
results_indep[c] = {}
results_tog[c] = {}
results_t[c] = {}
for label, data in data_m.items():
results_indep[c][label] = 0
results_tog[c][label] = 0
results_t[c][label] = 0
sc_idxs = get_idxs(sc_genes, gene_name)
fb_idxs = get_idxs(fb_genes, gene_name)
mb_idxs = get_idxs(mb_genes, gene_name)
hb_idxs = get_idxs(hb_genes, gene_name)
prolif_idxs = get_idxs(prolif, gene_name)
for data_m in data_ms:
for label, data in data_m.items():
# Do it together
#grp1, grp2, data, cond_label, data_label
get_res(sc_idxs, fb_idxs, data, 'SC and FB', label)
get_res(sc_idxs, mb_idxs, data, 'SC and MB', label)
get_res(sc_idxs, hb_idxs, data, 'SC and HB', label)
get_res(sc_idxs, prolif_idxs, data, 'SC and Pro.', label)
get_res(mb_idxs, prolif_idxs, data, 'MB and Pro.', label)
get_res(mb_idxs, hb_idxs, data, 'MB and FB', label)
get_res(mb_idxs, hb_idxs, data, 'MB and HB', label)
get_res(hb_idxs, fb_idxs, data, 'HB and FB', label)
get_res(hb_idxs, prolif_idxs, data, 'HB and Pro.', label)
get_res(fb_idxs, prolif_idxs, data, 'FB and Pro.', label)
###############################################################################
# #
# This program is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation, either version 3 of the License, or #
# (at your option) any later version. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program. If not, see <http://www.gnu.org/licenses/>. #
# #
###############################################################################
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sciviso import Vis
class Barchart(Vis):
def __init__(self, df: pd.DataFrame, x: object, y: object, title='', xlabel='', ylabel='', hue=None, order=None,
hue_order=None, figsize=(1.0, 1.0), title_font_size=8, label_font_size=6, title_font_weight=700,
errwidth=0, linewidth=1, edgecolor="k"):
super().__init__(df, figsize=figsize, title_font_size=title_font_size, label_font_size=label_font_size,
title_font_weight=title_font_weight)
super().__init__(df)
self.df = df
self.x = x
self.title = title
self.hue = hue
self.order = order
self.hue_order = hue_order
self.label = 'barchart'
self.xlabel = xlabel
self.ylabel = ylabel
self.errwidth = errwidth
self.linewidth =linewidth
self.edgecolor = edgecolor
# '#483873', '#1BD8A6', '#B117B7',
sci_colour = ['#FFC107', '#016957', '#9785C0',
'#D09139', '#338A03', '#FF69A1', '#5930B1', '#FFE884', '#35B567', '#1E88E5',
'#ACAD60', '#A2FFB4', '#B618F5']
self.palette = sns.color_palette(sci_colour)
def plot(self):
x, y, hue_order, order, hue = self.x, self.y, self.hue_order, self.order, self.hue
plt.rcParams['figure.figsize'] = (1.0, 1.5)
sns.set(rc={'figure.figsize': (1.0, 1.5), 'font.family': 'sans-serif',
'font.sans-serif': 'Arial', 'font.size': 6}, style=self.style)
# First lets check whether we were passed lists or strings for our y and x arrays
if not isinstance(x, str) and not isinstance(y, str):
vis_df = pd.DataFrame()
vis_df['x'] = x
vis_df['y'] = y
x = 'x'
y = 'y'
if hue is not None:
vis_df['colour'] = hue
hue = 'colour'
if order is None:
order = list(set(vis_df['x'].values))
order.sort()
else:
vis_df = self.df
# set the orders
if hue_order is None and hue is not None:
hue_order = list(set(vis_df[hue].values))
hue_order.sort()
if order is None:
order = list(set(vis_df[x].values))
order.sort()
ax = sns.barplot(data=vis_df, x=x, y=y, hue=hue, hue_order=hue_order, order=order, palette=self.palette,
linewidth=self.linewidth, alpha=0.8, errwidth=self.errwidth)
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=self.label_font_size)
ax.tick_params(labelsize=self.label_font_size)
ax.set_ylim(0, 100)
self.add_labels()
self.set_ax_params(ax)
return ax
def set_ax_params(self, ax):
ax.tick_params(direction='out', length=2, width=0.5)
ax.spines['bottom'].set_linewidth(0.5)
ax.spines['top'].set_linewidth(0)
ax.spines['left'].set_linewidth(0.5)
ax.spines['right'].set_linewidth(0)
ax.set_xticklabels(ax.get_xticklabels(), weight = 'bold')
ax.tick_params(labelsize=self.label_font_size)
ax.tick_params(axis='x', which='major', pad=2.0)
ax.tick_params(axis='y', which='major', pad=2.0)
for test_label, values in results_tog.items():
v_df = pd.DataFrame()
x = []
y = []
for run_lbl, value in values.items():
if 'PCA' not in run_lbl and 'PHATE' not in run_lbl:
if run_lbl == 'VAE-lin':
x.append('VAE shallow')
elif run_lbl == 'VAE-deep':
x.append('VAE deep')
elif run_lbl == 'TSNE':
x.append('tSNE')
else:
x.append(run_lbl)
if test_label == 'FB and Pro.' or test_label == 'SC and HB' or test_label == 'MB and Pro.' or test_label == 'MB and FB' or test_label == 'MB and HB':
y.append(100 - ((value/30) * 100))
else:
y.append((value/30) * 100)
v_df['x'] = x
v_df['y'] = y
bc = Barchart(v_df, 'x', 'y',title=test_label, order=['VAE deep', 'VAE shallow', 'UMAP', 'tSNE'])
bc.plot()
save_fig(f'Bar_{test_label}')
plt.show()
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
No handles with labels found to put in legend.
def sig_dist_bp(grp1_idxs, grp2_idxs, data, grp1_label, grp2_label, title, box_colors):
centroid_grp1 = np.mean(data[grp1_idxs, :], axis=0)
grp_1_within_dist = np.sqrt(np.sum((data[grp1_idxs, :] - centroid_grp1) **2, axis=1))
centroid_grp2 = np.mean(data[grp2_idxs, :], axis=0)
grp_2_within_dist = np.sqrt(np.sum((data[grp2_idxs, :] - centroid_grp2) **2, axis=1))
all_idxs = grp1_idxs + grp2_idxs
centroid = np.mean(data[all_idxs, :], axis=0)
between_dist = np.sqrt(np.sum((data[all_idxs, :] - centroid) **2, axis=1))
box_df = pd.DataFrame()
all_label = f'{grp1_label.split(" ")[0]} & {grp2_label}'
box_df['Conditions'] = [grp1_label]*len(grp_1_within_dist) + [grp2_label]*len(grp_2_within_dist) + [all_label]*len(between_dist)
box_df['Values'] = list(grp_1_within_dist) + list(grp_2_within_dist) + list(between_dist)
dists = np.array(box_df['Values'].values)
labels = np.array(box_df['Conditions'].values)
print(f'---------------- {title} {grp1_label} {grp2_label} --------------------')
print(f'{grp1_label} vs {grp2_label} {stats.ttest_ind(dists[np.where(labels == grp1_label)], dists[np.where(labels == grp2_label)])}')
print(f'{grp1_label} vs {grp1_label}-{grp2_label} {stats.ttest_ind(dists[np.where(labels == grp1_label)], dists[np.where(labels == all_label)])}')
print(f'{grp2_label} vs {grp1_label}-{grp2_label} {stats.ttest_ind(dists[np.where(labels == grp2_label)], dists[np.where(labels == all_label)])}')
print('----------------------------------------')
boxplot = Boxplot(box_df, "Conditions", "Values", title=f'{title} {grp1_label} {grp2_label}', add_stats=True, add_dots=True,
order=[grp1_label, grp2_label, f'{grp1_label.split(" ")[0]} & {grp2_label}'], box_colors=box_colors)
boxplot.palette = sci_colour
boxplot.stat_method = 't-test_ind'
boxplot.plot()
save_fig(f't-test_{title}-{grp1_label}-{grp2_label}')
plt.show()
for label, data in data_ms[0].items():
# plot the first one on a scatter plot for visualisation purposes
vae_vis.plot_values_on_scatters(df_input, gene_name, marker_labels_sep,
gene_markers_sep, output_dir=fig_dir, plt_bg=False, vae_data=data,
fig_type="svg",
title=f'Markers sep {label} {experiment_name}',
show_plt=False, save_fig=False, color_map=color_map, angle_plot=90)
plt.show()
pro_colour = '#9785C0'
sig_dist_bp(sc_idxs, fb_idxs, data_m[label], 'SC', 'FB', label, [fb_color, sc_color, grey])
sig_dist_bp(fb_idxs, mb_idxs, data_m[label], 'FB', 'MB', label, [fb_color, mb_color, grey])
sig_dist_bp(fb_idxs, hb_idxs, data_m[label], 'FB', 'HB', label, [fb_color, hb_color, grey])
sig_dist_bp(mb_idxs, sc_idxs, data_m[label], 'MB', 'SC', label, [mb_color, sc_color, grey])
sig_dist_bp(mb_idxs, hb_idxs, data_m[label], 'MB', 'HB', label, [fb_color, sc_color, grey])
sig_dist_bp(hb_idxs, sc_idxs, data_m[label], 'HB', 'SC', label, [hb_color, sc_color, grey])
sig_dist_bp(fb_idxs, prolif_idxs, data_m[label], 'FB', 'Pro.', label, [fb_color, pro_colour, grey])
sig_dist_bp(mb_idxs, prolif_idxs, data_m[label], 'MB', 'Pro.', label, [mb_color, pro_colour, grey])
sig_dist_bp(hb_idxs, prolif_idxs, data_m[label], 'HB', 'Pro.', label, [hb_color, pro_colour, grey])
sig_dist_bp(sc_idxs, prolif_idxs, data_m[label], 'SC', 'Pro.', label, [sc_color, pro_colour, grey])
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 27.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
---------------- VAE-deep SC FB -------------------- SC vs FB Ttest_indResult(statistic=1.465415384270918, pvalue=0.15479722168010257) SC vs SC-FB Ttest_indResult(statistic=-6.852495182975661, pvalue=1.8812996214418804e-08) FB vs SC-FB Ttest_indResult(statistic=-6.334888088353937, pvalue=2.4856279563948777e-07) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=4.644e-01 stat=1.465e+00 FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=7.457e-07 stat=-6.335e+00 SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=5.644e-08 stat=-6.852e+00
No handles with labels found to put in legend.
---------------- VAE-deep FB MB -------------------- FB vs MB Ttest_indResult(statistic=-1.8477570573626867, pvalue=0.08751211510829689) FB vs FB-MB Ttest_indResult(statistic=-1.2521043461094474, pvalue=0.22311547213412358) MB vs FB-MB Ttest_indResult(statistic=0.9187519032509673, pvalue=0.370373574674694) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=2.625e-01 stat=-1.848e+00 MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=9.188e-01 FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=6.693e-01 stat=-1.252e+00
---------------- VAE-deep FB HB --------------------
No handles with labels found to put in legend.
FB vs HB Ttest_indResult(statistic=-4.1366795848954165, pvalue=0.0013789673555127345) FB vs FB-HB Ttest_indResult(statistic=-1.814677434667457, pvalue=0.08323379592016182) HB vs FB-HB Ttest_indResult(statistic=1.1672410038243244, pvalue=0.26021482348887254) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=4.137e-03 stat=-4.137e+00 HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=7.806e-01 stat=1.167e+00 FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=2.497e-01 stat=-1.815e+00
---------------- VAE-deep MB SC -------------------- MB vs SC Ttest_indResult(statistic=1.4772677270409198, pvalue=0.15444483805423154) MB vs MB-SC Ttest_indResult(statistic=-0.5245846226479846, pvalue=0.6043163452855129) SC vs MB-SC Ttest_indResult(statistic=-2.7132929847714453, pvalue=0.009864881246446645) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=4.633e-01 stat=1.477e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 17.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=2.959e-02 stat=-2.713e+00 MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-5.246e-01
---------------- VAE-deep MB HB -------------------- MB vs HB Ttest_indResult(statistic=-0.713087547103354, pvalue=0.4988674066411293) MB vs MB-HB Ttest_indResult(statistic=-0.4826368937188724, pvalue=0.638038222262647) HB vs MB-HB Ttest_indResult(statistic=0.24477649521453204, pvalue=0.8111356412616538) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-7.131e-01
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=2.448e-01 MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.826e-01
---------------- VAE-deep HB SC -------------------- HB vs SC Ttest_indResult(statistic=3.501068211779919, pvalue=0.00224955263807113) HB vs HB-SC Ttest_indResult(statistic=0.831316828400836, pvalue=0.4139893806188496) SC vs HB-SC Ttest_indResult(statistic=-1.9910155123119673, pvalue=0.05370511591609788) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=6.749e-03 stat=3.501e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 9.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.611e-01 stat=-1.991e+00 HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=8.313e-01
---------------- VAE-deep FB Pro. -------------------- FB vs Pro. Ttest_indResult(statistic=-2.4554773657399576, pvalue=0.021069582566877302) FB vs FB-Pro. Ttest_indResult(statistic=-6.081857472435021, pvalue=5.403297224686054e-07) Pro. vs FB-Pro. Ttest_indResult(statistic=-1.8434084509316222, pvalue=0.0720113122300351) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=6.321e-02 stat=-2.455e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 28.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.160e-01 stat=-1.843e+00 FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.621e-06 stat=-6.082e+00
---------------- VAE-deep MB Pro. -------------------- MB vs Pro. Ttest_indResult(statistic=-0.19339630822688209, pvalue=0.8485069371938883) MB vs MB-Pro. Ttest_indResult(statistic=-1.4505604973329764, pvalue=0.1588605151977384) Pro. vs MB-Pro. Ttest_indResult(statistic=-1.9192655430106675, pvalue=0.0622891808760423) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-1.934e-01
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 8.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.869e-01 stat=-1.919e+00 MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=4.766e-01 stat=-1.451e+00
---------------- VAE-deep HB Pro. -------------------- HB vs Pro. Ttest_indResult(statistic=0.6787463123930841, pvalue=0.5050776816549938) HB vs HB-Pro. Ttest_indResult(statistic=-0.48953485002892716, pvalue=0.6289077026794281) Pro. vs HB-Pro. Ttest_indResult(statistic=-1.8701029272681475, pvalue=0.06918778269192202) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=6.787e-01
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.076e-01 stat=-1.870e+00 HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.895e-01
---------------- VAE-deep SC Pro. --------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 22.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
SC vs Pro. Ttest_indResult(statistic=-2.218756316335035, pvalue=0.03328240587030609) SC vs SC-Pro. Ttest_indResult(statistic=-16.376548187212467, pvalue=3.582819584663037e-22) Pro. vs SC-Pro. Ttest_indResult(statistic=-9.909644854626288, pvalue=1.420871818596834e-13) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 27.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
*: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=9.985e-02 stat=-2.219e+00 Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=4.263e-13 stat=-9.910e+00 SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.075e-21 stat=-1.638e+01
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
---------------- VAE-lin SC FB -------------------- SC vs FB Ttest_indResult(statistic=1.2258535090577791, pvalue=0.23124174276882076) SC vs SC-FB Ttest_indResult(statistic=-3.0391941852313016, pvalue=0.003983437328897631) FB vs SC-FB Ttest_indResult(statistic=-3.7249606713581973, pvalue=0.000667066291543137) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=6.937e-01 stat=1.226e+00 FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=2.001e-03 stat=-3.725e+00 SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=1.195e-02 stat=-3.039e+00
---------------- VAE-lin FB MB -------------------- FB vs MB Ttest_indResult(statistic=-1.5383516529791994, pvalue=0.147940942078687) FB vs FB-MB Ttest_indResult(statistic=-1.2610831701229774, pvalue=0.2199210009510995) MB vs FB-MB Ttest_indResult(statistic=0.6869855072753703, pvalue=0.5008477369479887) ----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=4.438e-01 stat=-1.538e+00 MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=6.870e-01 FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=6.598e-01 stat=-1.261e+00
---------------- VAE-lin FB HB -------------------- FB vs HB Ttest_indResult(statistic=-2.2694350999955946, pvalue=0.04248130135064402) FB vs FB-HB Ttest_indResult(statistic=-1.6272459137522848, pvalue=0.11792379367332484) HB vs FB-HB Ttest_indResult(statistic=0.38649404085129047, pvalue=0.7042223585912493) ----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.274e-01 stat=-2.269e+00 HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=3.865e-01 FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=3.538e-01 stat=-1.627e+00
---------------- VAE-lin MB SC -------------------- MB vs SC Ttest_indResult(statistic=0.9306041397657198, pvalue=0.36263380062780404) MB vs MB-SC Ttest_indResult(statistic=-0.036502138772067545, pvalue=0.9711607905449361) SC vs MB-SC Ttest_indResult(statistic=-1.350975303254348, pvalue=0.18448956317815834) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=9.306e-01 SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=5.535e-01 stat=-1.351e+00 MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-3.650e-02
---------------- VAE-lin MB HB -------------------- MB vs HB Ttest_indResult(statistic=-0.07398576018508644, pvalue=0.9430915573279666) MB vs MB-HB Ttest_indResult(statistic=-0.22173618315963317, pvalue=0.8282478536724209) HB vs MB-HB Ttest_indResult(statistic=-0.14860182687169554, pvalue=0.8845570290772059) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-7.399e-02
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-1.486e-01 MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-2.217e-01
---------------- VAE-lin HB SC -------------------- HB vs SC Ttest_indResult(statistic=1.1642649830470895, pvalue=0.2580175312051193) HB vs HB-SC Ttest_indResult(statistic=0.824166583610865, pvalue=0.41796065724057074) SC vs HB-SC Ttest_indResult(statistic=-0.4374782797154119, pvalue=0.6642409648728969) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=7.741e-01 stat=1.164e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.375e-01 HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=8.242e-01
---------------- VAE-lin FB Pro. -------------------- FB vs Pro. Ttest_indResult(statistic=-2.4508018880511755, pvalue=0.02129187800633666) FB vs FB-Pro. Ttest_indResult(statistic=-5.911312068576774, pvalue=9.129570891204239e-07) Pro. vs FB-Pro. Ttest_indResult(statistic=-2.5217364148814996, pvalue=0.01537268219343367) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=6.388e-02 stat=-2.451e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=4.612e-02 stat=-2.522e+00 FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.739e-06 stat=-5.911e+00
---------------- VAE-lin MB Pro. -------------------- MB vs Pro. Ttest_indResult(statistic=-0.4709873802340494, pvalue=0.6425054845317733) MB vs MB-Pro. Ttest_indResult(statistic=-1.7990254113189708, pvalue=0.08363356075031039) Pro. vs MB-Pro. Ttest_indResult(statistic=-2.0534010104823834, pvalue=0.046783149739656645) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.710e-01
No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.403e-01 stat=-2.053e+00 MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.509e-01 stat=-1.799e+00
---------------- VAE-lin HB Pro. -------------------- HB vs Pro. Ttest_indResult(statistic=-0.37007364456111946, pvalue=0.7152176266838082) HB vs HB-Pro. Ttest_indResult(statistic=-1.5173634179246154, pvalue=0.14223806431137537) Pro. vs HB-Pro. Ttest_indResult(statistic=-2.2009034680850093, pvalue=0.03389073670435528) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-3.701e-01
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.017e-01 stat=-2.201e+00 HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=4.267e-01 stat=-1.517e+00
---------------- VAE-lin SC Pro. --------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 27.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
SC vs Pro. Ttest_indResult(statistic=-2.099855601849475, pvalue=0.043236912469487875) SC vs SC-Pro. Ttest_indResult(statistic=-11.184168863900716, pvalue=1.893978504016835e-15) Pro. vs SC-Pro. Ttest_indResult(statistic=-6.931480960094009, pvalue=6.364447928458082e-09) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.297e-01 stat=-2.100e+00
No handles with labels found to put in legend.
Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.909e-08 stat=-6.931e+00 SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=5.682e-15 stat=-1.118e+01
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 21.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
---------------- PCA SC FB -------------------- SC vs FB Ttest_indResult(statistic=-1.2995494799884133, pvalue=0.2051605193151418) SC vs SC-FB Ttest_indResult(statistic=-3.564731784933484, pvalue=0.0008916941152581848) FB vs SC-FB Ttest_indResult(statistic=-1.7000401229482058, pvalue=0.0977490265194618) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=6.155e-01 stat=-1.300e+00 FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=2.932e-01 stat=-1.700e+00 SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=2.675e-03 stat=-3.565e+00
---------------- PCA FB MB -------------------- FB vs MB Ttest_indResult(statistic=-0.8331827813719793, pvalue=0.4197865321572436) FB vs FB-MB Ttest_indResult(statistic=-0.588871468516919, pvalue=0.5616870309565367) MB vs FB-MB Ttest_indResult(statistic=0.4264165416498853, pvalue=0.6748604262985933) ---------------------------------------- p-value annotation legend:
No handles with labels found to put in legend.
ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.332e-01 MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=4.264e-01 FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-5.889e-01
---------------- PCA FB HB -------------------- FB vs HB Ttest_indResult(statistic=-2.22308121636322, pvalue=0.04618193786618191) FB vs FB-HB Ttest_indResult(statistic=-1.02476462964014, pvalue=0.3166108879045668) HB vs FB-HB Ttest_indResult(statistic=0.8156411106830408, pvalue=0.4266815074869178) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.385e-01 stat=-2.223e+00
No handles with labels found to put in legend.
HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=8.156e-01 FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=9.498e-01 stat=-1.025e+00
---------------- PCA MB SC -------------------- MB vs SC Ttest_indResult(statistic=1.9329939865523886, pvalue=0.06683410263144207) MB vs MB-SC Ttest_indResult(statistic=0.3235679598623498, pvalue=0.7488534627668906) SC vs MB-SC Ttest_indResult(statistic=-1.8892021703623298, pvalue=0.06631646640496296) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=2.005e-01 stat=1.933e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.989e-01 stat=-1.889e+00 MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=3.236e-01
---------------- PCA MB HB -------------------- MB vs HB Ttest_indResult(statistic=-0.9361619617226498, pvalue=0.38035393337492623) MB vs MB-HB Ttest_indResult(statistic=-1.0107038325793454, pvalue=0.3321041405760581) HB vs MB-HB Ttest_indResult(statistic=0.07069365697925167, pvalue=0.9449104508006051) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-9.362e-01
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=7.069e-02 MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=9.963e-01 stat=-1.011e+00
---------------- PCA HB SC -------------------- HB vs SC Ttest_indResult(statistic=3.582853812880347, pvalue=0.0018609852890687825) HB vs HB-SC Ttest_indResult(statistic=2.0044796296106533, pvalue=0.05642688473698121) SC vs HB-SC Ttest_indResult(statistic=-1.5184455139578321, pvalue=0.1371789591771647) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=5.583e-03 stat=3.583e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 9.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=4.115e-01 stat=-1.518e+00 HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.693e-01 stat=2.004e+00
---------------- PCA FB Pro. -------------------- FB vs Pro. Ttest_indResult(statistic=-2.2642990322057197, pvalue=0.03212644487350482) FB vs FB-Pro. Ttest_indResult(statistic=-6.309673624323211, pvalue=2.685278230136594e-07) Pro. vs FB-Pro. Ttest_indResult(statistic=-2.530227233682394, pvalue=0.015052651760020071) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 17.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=9.638e-02 stat=-2.264e+00
No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=4.516e-02 stat=-2.530e+00 FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=8.056e-07 stat=-6.310e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
---------------- PCA MB Pro. -------------------- MB vs Pro. Ttest_indResult(statistic=-1.2484100928200474, pvalue=0.2256205619646796) MB vs MB-Pro. Ttest_indResult(statistic=-2.9377021800930074, pvalue=0.006842719879661337) Pro. vs MB-Pro. Ttest_indResult(statistic=-1.945334639941668, pvalue=0.05896887307402261) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=6.769e-01 stat=-1.248e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.769e-01 stat=-1.945e+00 MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.053e-02 stat=-2.938e+00
---------------- PCA HB Pro. -------------------- HB vs Pro. Ttest_indResult(statistic=-0.5870702922892078, pvalue=0.5637257434484357) HB vs HB-Pro. Ttest_indResult(statistic=-1.7614105106660767, pvalue=0.09090552459162164) Pro. vs HB-Pro. Ttest_indResult(statistic=-2.017631978362012, pvalue=0.05073210198442755) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-5.871e-01
No handles with labels found to put in legend.
Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.522e-01 stat=-2.018e+00 HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.727e-01 stat=-1.761e+00
---------------- PCA SC Pro. -------------------- SC vs Pro. Ttest_indResult(statistic=-3.592631939942266, pvalue=0.0010226928206347485) SC vs SC-Pro. Ttest_indResult(statistic=-16.71032605181185, pvalue=1.4790863735270798e-22) Pro. vs SC-Pro. Ttest_indResult(statistic=-5.8596485880037745, pvalue=3.2046941476966316e-07) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=3.068e-03 stat=-3.593e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 11.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 25.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=9.614e-07 stat=-5.860e+00 SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=4.437e-22 stat=-1.671e+01
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 55.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 30.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 60.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
---------------- UMAP SC FB -------------------- SC vs FB Ttest_indResult(statistic=-2.3550844974632112, pvalue=0.02634432522201786) SC vs SC-FB Ttest_indResult(statistic=-4.878900683226698, pvalue=1.4368466937818415e-05) FB vs SC-FB Ttest_indResult(statistic=-1.146775088516953, pvalue=0.2590395837302274) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=7.903e-02 stat=-2.355e+00 FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=7.771e-01 stat=-1.147e+00 SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=4.311e-05 stat=-4.879e+00
---------------- UMAP FB MB -------------------- FB vs MB Ttest_indResult(statistic=-0.42539830460193684, pvalue=0.6775038631600655) FB vs FB-MB Ttest_indResult(statistic=-0.8655449061456219, pvalue=0.395678687279831) MB vs FB-MB Ttest_indResult(statistic=-0.2696024899480067, pvalue=0.7905324762536508) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 20.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.254e-01 MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-2.696e-01 FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.655e-01
---------------- UMAP FB HB -------------------- FB vs HB Ttest_indResult(statistic=-1.136720482652885, pvalue=0.27784573432438214) FB vs FB-HB Ttest_indResult(statistic=-0.6766648404151911, pvalue=0.5056715583130658) HB vs FB-HB Ttest_indResult(statistic=0.46772028309621144, pvalue=0.6462919397715152) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 20.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 21.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00
No handles with labels found to put in legend.
*: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=8.335e-01 stat=-1.137e+00 HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=4.677e-01 FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-6.767e-01
---------------- UMAP MB SC -------------------- MB vs SC Ttest_indResult(statistic=5.901378167241427, pvalue=7.393506643511739e-06) MB vs MB-SC Ttest_indResult(statistic=-0.32816948389294914, pvalue=0.7454117909723175) SC vs MB-SC Ttest_indResult(statistic=-4.368999008537589, pvalue=8.949626754144127e-05) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 44.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 47.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=2.218e-05 stat=5.901e+00 SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=2.685e-04 stat=-4.369e+00 MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-3.282e-01
---------------- UMAP MB HB -------------------- MB vs HB Ttest_indResult(statistic=-1.3457520246869854, pvalue=0.22033718898493246) MB vs MB-HB Ttest_indResult(statistic=-1.7037008221394974, pvalue=0.11417049887161534) HB vs MB-HB Ttest_indResult(statistic=-0.7006934874294773, pvalue=0.49804812620177896) ---------------------------------------- p-value annotation legend:
No handles with labels found to put in legend.
ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=6.610e-01 stat=-1.346e+00 HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-7.007e-01 MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=3.425e-01 stat=-1.704e+00
---------------- UMAP HB SC -------------------- HB vs SC Ttest_indResult(statistic=9.781570532272346, pvalue=4.580640179986176e-09) HB vs HB-SC Ttest_indResult(statistic=2.3591057751364453, pvalue=0.02679691463165309) SC vs HB-SC Ttest_indResult(statistic=-2.5606771435535234, pvalue=0.014542959222688223) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 38.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 31.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.374e-08 stat=9.782e+00 SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=4.363e-02 stat=-2.561e+00 HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=8.039e-02 stat=2.359e+00
---------------- UMAP FB Pro. -------------------- FB vs Pro. Ttest_indResult(statistic=-6.66418941923738, pvalue=4.539075699008851e-07) FB vs FB-Pro. Ttest_indResult(statistic=-7.605411692594524, pvalue=5.36121237110632e-09) Pro. vs FB-Pro. Ttest_indResult(statistic=-1.0754522255024515, pvalue=0.28803430719309725) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 40.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 25.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.362e-06 stat=-6.664e+00 Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=8.641e-01 stat=-1.075e+00 FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.608e-08 stat=-7.605e+00
---------------- UMAP MB Pro. -------------------- MB vs Pro. Ttest_indResult(statistic=-4.633856138407465, pvalue=0.00014288470728259926) MB vs MB-Pro. Ttest_indResult(statistic=-5.042688741336938, pvalue=3.002622603619258e-05) Pro. vs MB-Pro. Ttest_indResult(statistic=-0.8139014000114384, pvalue=0.42064320216592865) ----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=4.287e-04 stat=-4.634e+00 Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.139e-01 MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=9.008e-05 stat=-5.043e+00
---------------- UMAP HB Pro. -------------------- HB vs Pro. Ttest_indResult(statistic=-3.925202631357916, pvalue=0.0008379418182200716) HB vs HB-Pro. Ttest_indResult(statistic=-4.021405446677118, pvalue=0.0004991582753703138) Pro. vs HB-Pro. Ttest_indResult(statistic=-1.0198711029861514, pvalue=0.3142390521468771) ----------------------------------------
No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=2.514e-03 stat=-3.925e+00 Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=9.427e-01 stat=-1.020e+00 HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.497e-03 stat=-4.021e+00
---------------- UMAP SC Pro. -------------------- SC vs Pro. Ttest_indResult(statistic=-9.853718810939363, pvalue=1.6990403990373034e-11) SC vs SC-Pro. Ttest_indResult(statistic=-10.430215506455044, pvalue=2.378562421570917e-14) Pro. vs SC-Pro. Ttest_indResult(statistic=-1.409483351315476, pvalue=0.16464508682124834) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 72.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 44.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=5.097e-11 stat=-9.854e+00 Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=4.939e-01 stat=-1.409e+00 SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=7.136e-14 stat=-1.043e+01
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 55.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 20.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 28.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
---------------- PHATE SC FB -------------------- SC vs FB Ttest_indResult(statistic=-1.0191890574164366, pvalue=0.31750376199596386) SC vs SC-FB Ttest_indResult(statistic=-6.893450560450847, pvalue=1.638140982141908e-08) FB vs SC-FB Ttest_indResult(statistic=-4.004956065418169, pvalue=0.00029715435548181764) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=9.525e-01 stat=-1.019e+00 FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=8.915e-04 stat=-4.005e+00 SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=4.914e-08 stat=-6.893e+00
---------------- PHATE FB MB -------------------- FB vs MB Ttest_indResult(statistic=-0.008090247461819994, pvalue=0.9936678218118067) FB vs FB-MB Ttest_indResult(statistic=-0.10433772646495926, pvalue=0.9178058667437479) MB vs FB-MB Ttest_indResult(statistic=-0.08748813000864061, pvalue=0.9312494777420808) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.090e-03 MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.749e-02 FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-1.043e-01
---------------- PHATE FB HB -------------------- FB vs HB Ttest_indResult(statistic=-0.5608169833933422, pvalue=0.5852419569319545) FB vs FB-HB Ttest_indResult(statistic=-0.7267386110318872, pvalue=0.47504851894542055) HB vs FB-HB Ttest_indResult(statistic=-0.09470930787702049, pvalue=0.9257216947752891) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 10.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-5.608e-01 HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-9.471e-02 FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-7.267e-01
---------------- PHATE MB SC -------------------- MB vs SC Ttest_indResult(statistic=1.368588531145401, pvalue=0.18559197990248347) MB vs MB-SC Ttest_indResult(statistic=-1.7842280750756356, pvalue=0.08606260645407866) SC vs MB-SC Ttest_indResult(statistic=-4.225746927478239, pvalue=0.0001386255718839648) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 8.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=5.568e-01 stat=1.369e+00 SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=4.159e-04 stat=-4.226e+00 MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=2.582e-01 stat=-1.784e+00
---------------- PHATE MB HB -------------------- MB vs HB Ttest_indResult(statistic=-0.9782850154083179, pvalue=0.360524317180079) MB vs MB-HB Ttest_indResult(statistic=-1.9130854260010326, pvalue=0.07989299754384167) HB vs MB-HB Ttest_indResult(statistic=-1.0694051114513252, pvalue=0.3077924990414019) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-9.783e-01
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=9.234e-01 stat=-1.069e+00 MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=2.397e-01 stat=-1.913e+00
---------------- PHATE HB SC -------------------- HB vs SC Ttest_indResult(statistic=2.7286822364214, pvalue=0.012938513102590837) HB vs HB-SC Ttest_indResult(statistic=0.28542352300991514, pvalue=0.7777685171186905) SC vs HB-SC Ttest_indResult(statistic=-2.383888270635071, pvalue=0.0222300470103602) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=3.882e-02 stat=2.729e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=6.669e-02 stat=-2.384e+00 HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=2.854e-01
---------------- PHATE FB Pro. -------------------- FB vs Pro. Ttest_indResult(statistic=-5.84019269058808, pvalue=3.721409261077368e-06) FB vs FB-Pro. Ttest_indResult(statistic=-9.415107150488991, pvalue=3.0310916656606977e-11) Pro. vs FB-Pro. Ttest_indResult(statistic=-3.9308160809577637, pvalue=0.0002958091054266404) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 20.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.116e-05 stat=-5.840e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 17.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=8.874e-04 stat=-3.931e+00 FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=9.093e-11 stat=-9.415e+00
---------------- PHATE MB Pro. -------------------- MB vs Pro. Ttest_indResult(statistic=-4.736319607054566, pvalue=0.00011196405825435024) MB vs MB-Pro. Ttest_indResult(statistic=-5.703664599609176, pvalue=5.305336352992016e-06) Pro. vs MB-Pro. Ttest_indResult(statistic=-2.623808901355207, pvalue=0.012347316848054419) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=3.359e-04 stat=-4.736e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 5.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 8.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.704e-02 stat=-2.624e+00 MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.592e-05 stat=-5.704e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
---------------- PHATE HB Pro. -------------------- HB vs Pro. Ttest_indResult(statistic=-3.7763166792342973, pvalue=0.0011862878979104615) HB vs HB-Pro. Ttest_indResult(statistic=-3.556909566517591, pvalue=0.0016000631561495203) Pro. vs HB-Pro. Ttest_indResult(statistic=-2.6601695768201012, pvalue=0.011374539115971201) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 18.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=3.559e-03 stat=-3.776e+00 Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=3.412e-02 stat=-2.660e+00 HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=4.800e-03 stat=-3.557e+00
---------------- PHATE SC Pro. -------------------- SC vs Pro. Ttest_indResult(statistic=-9.8233830589069, pvalue=1.8374717432370123e-11) SC vs SC-Pro. Ttest_indResult(statistic=-20.115439546168364, pvalue=3.4234577669915293e-26) Pro. vs SC-Pro. Ttest_indResult(statistic=-9.348799039743355, pvalue=1.0088177465997982e-12) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 61.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 16.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 38.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=5.512e-11 stat=-9.823e+00 Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=3.026e-12 stat=-9.349e+00 SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=1.027e-25 stat=-2.012e+01
<Figure size 108x108 with 0 Axes>
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 55.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 40.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 53.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
---------------- TSNE SC FB -------------------- SC vs FB Ttest_indResult(statistic=-2.3998862414306, pvalue=0.023856423372871822) SC vs SC-FB Ttest_indResult(statistic=-7.291005077769237, pvalue=4.290927257226062e-09) FB vs SC-FB Ttest_indResult(statistic=-2.417168218356927, pvalue=0.02083410337382203) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. FB: t-test independent samples with Bonferroni correction, P_val=7.157e-02 stat=-2.400e+00 FB v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=6.250e-02 stat=-2.417e+00 SC v.s. SC & FB: t-test independent samples with Bonferroni correction, P_val=1.287e-08 stat=-7.291e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 30.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 13.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
---------------- TSNE FB MB -------------------- FB vs MB Ttest_indResult(statistic=-0.37281069320951277, pvalue=0.715297135414851) FB vs FB-MB Ttest_indResult(statistic=-1.0442420002348785, pvalue=0.3072251825877592) MB vs FB-MB Ttest_indResult(statistic=-0.49856507418539364, pvalue=0.6241232763182525) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-3.728e-01 MB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-4.986e-01 FB v.s. FB & MB: t-test independent samples with Bonferroni correction, P_val=9.217e-01 stat=-1.044e+00
---------------- TSNE FB HB -------------------- FB vs HB Ttest_indResult(statistic=-1.539449158210592, pvalue=0.1496381371291977) FB vs FB-HB Ttest_indResult(statistic=-1.1904933709576022, pvalue=0.24654035114435652) HB vs FB-HB Ttest_indResult(statistic=0.5257902162109782, pvalue=0.6062453491633906) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 30.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. HB: t-test independent samples with Bonferroni correction, P_val=4.489e-01 stat=-1.539e+00 HB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=5.258e-01 FB v.s. FB & HB: t-test independent samples with Bonferroni correction, P_val=7.396e-01 stat=-1.190e+00
---------------- TSNE MB SC -------------------- MB vs SC Ttest_indResult(statistic=5.247262215297781, pvalue=3.350773245241739e-05) MB vs MB-SC Ttest_indResult(statistic=-0.09750569403461012, pvalue=0.9230725966062266) SC vs MB-SC Ttest_indResult(statistic=-4.209777617486438, pvalue=0.00014551860698258239) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 44.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 43.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.005e-04 stat=5.247e+00 SC v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=4.366e-04 stat=-4.210e+00 MB v.s. MB & SC: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-9.751e-02
---------------- TSNE MB HB -------------------- MB vs HB Ttest_indResult(statistic=-1.831941758679019, pvalue=0.10963282179269813) MB vs MB-HB Ttest_indResult(statistic=-1.5349261794171583, pvalue=0.15073694781170383) HB vs MB-HB Ttest_indResult(statistic=0.02516885109359279, pvalue=0.980371072532495) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. HB: t-test independent samples with Bonferroni correction, P_val=3.289e-01 stat=-1.832e+00
No handles with labels found to put in legend.
HB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=2.517e-02 MB v.s. MB & HB: t-test independent samples with Bonferroni correction, P_val=4.522e-01 stat=-1.535e+00
---------------- TSNE HB SC -------------------- HB vs SC Ttest_indResult(statistic=11.022644020199362, pvalue=6.007152489294897e-10) HB vs HB-SC Ttest_indResult(statistic=2.6097877363913433, pvalue=0.015360528263327759) SC vs HB-SC Ttest_indResult(statistic=-2.4975673694437717, pvalue=0.016952742409877576) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. SC: t-test independent samples with Bonferroni correction, P_val=1.802e-09 stat=1.102e+01
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 38.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 40.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
SC v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=5.086e-02 stat=-2.498e+00 HB v.s. HB & SC: t-test independent samples with Bonferroni correction, P_val=4.608e-02 stat=2.610e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 40.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 27.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
---------------- TSNE FB Pro. -------------------- FB vs Pro. Ttest_indResult(statistic=-5.279789098200505, pvalue=1.6082949474576737e-05) FB vs FB-Pro. Ttest_indResult(statistic=-10.199641710237753, pvalue=3.653800097771942e-12) Pro. vs FB-Pro. Ttest_indResult(statistic=-4.295796717464091, pvalue=9.460226014970065e-05) ---------------------------------------- p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 FB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=4.825e-05 stat=-5.280e+00
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 32.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
Pro. v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.838e-04 stat=-4.296e+00 FB v.s. FB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.096e-11 stat=-1.020e+01
---------------- TSNE MB Pro. -------------------- MB vs Pro. Ttest_indResult(statistic=-3.695118224212054, pvalue=0.001343592679834239) MB vs MB-Pro. Ttest_indResult(statistic=-5.84350272964791, pvalue=3.689619802094182e-06) Pro. vs MB-Pro. Ttest_indResult(statistic=-2.792340207507878, pvalue=0.008064830561535959) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 26.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 MB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=4.031e-03 stat=-3.695e+00 Pro. v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.419e-02 stat=-2.792e+00 MB v.s. MB & Pro.: t-test independent samples with Bonferroni correction, P_val=1.107e-05 stat=-5.844e+00
---------------- TSNE HB Pro. -------------------- HB vs Pro. Ttest_indResult(statistic=-2.729677093873864, pvalue=0.01291028660021382) HB vs HB-Pro. Ttest_indResult(statistic=-3.8682219149759285, pvalue=0.0007346257195254469) Pro. vs HB-Pro. Ttest_indResult(statistic=-2.445340339771659, pvalue=0.01921664617693632) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 18.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 HB v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=3.873e-02 stat=-2.730e+00 Pro. v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=5.765e-02 stat=-2.445e+00 HB v.s. HB & Pro.: t-test independent samples with Bonferroni correction, P_val=2.204e-03 stat=-3.868e+00
---------------- TSNE SC Pro. -------------------- SC vs Pro. Ttest_indResult(statistic=-8.663039773071757, pvalue=4.022141521662489e-10) SC vs SC-Pro. Ttest_indResult(statistic=-14.81743301193944, pvalue=2.6415051770699156e-20) Pro. vs SC-Pro. Ttest_indResult(statistic=-4.666028063823687, pvalue=2.1891918237584822e-05) ----------------------------------------
/Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 72.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) /Users/ariane/opt/miniconda3/envs/ml/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 52.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) No handles with labels found to put in legend.
p-value annotation legend: ns: 5.00e-02 < p <= 1.00e+00 *: 1.00e-02 < p <= 5.00e-02 **: 1.00e-03 < p <= 1.00e-02 ***: 1.00e-04 < p <= 1.00e-03 ****: p <= 1.00e-04 SC v.s. Pro.: t-test independent samples with Bonferroni correction, P_val=1.207e-09 stat=-8.663e+00 Pro. v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=6.568e-05 stat=-4.666e+00 SC v.s. SC & Pro.: t-test independent samples with Bonferroni correction, P_val=7.925e-20 stat=-1.482e+01
For each method for each dimension and use these to test for ORA
n_genes = 200
def save_gene_order_lst(data, test_label):
for n in range(0, num_nodes):
with open(f'{ora_dir}{test_label}_{n}-max_sig-3_{date}.csv', 'w+') as f:
f.write(gene_id + ',' + gene_name + '\n')
desc_sorted = (-data[:,n]).argsort() # Sort the genes by descending order
gene_names_sorted = df_training[gene_name].values[desc_sorted]
gene_ids_sorted = df_training[gene_id].values[desc_sorted]
for i in range(0, n_genes):
f.write(f'{gene_ids_sorted[i]},{gene_names_sorted[i]}\n')
with open(f'{ora_dir}{test_label}_{n}-min_sig-3_{date}.csv', 'w+') as f:
f.write(gene_id + ',' + gene_name + '\n')
desc_sorted = (data[:,n]).argsort() # Sort the genes by ascending order
gene_names_sorted = df_training[gene_name].values[desc_sorted]
gene_ids_sorted = df_training[gene_id].values[desc_sorted]
for i in range(0, n_genes):
f.write(f'{gene_ids_sorted[i]},{gene_names_sorted[i]}\n')
save_gene_order_lst(vae_data, 'VAE-deep')
save_gene_order_lst(vae_data_shallow, 'VAE-shallow')
save_gene_order_lst(pca_values, 'PCA')
save_gene_order_lst(umap_e, 'UMAP')
save_gene_order_lst(data_phate, 'PHATE')
save_gene_order_lst(data_tsne, 'TSNE')
We'll test each for functional enrichment
for label, data in data_ms[0].items():
for n in range(0, num_nodes):
with open(f'{ora_dir}{label}-{n + 1}_{experiment_name}_{date}.csv', 'w+') as f:
f.write(gene_id + ',value\n')
desc_sorted = (data[:,n]).argsort() # Sort the genes by descending order
gene_names_sorted = df[gene_id].values[desc_sorted]
vae_data_sorted = data[:,n][desc_sorted]
for i, g in enumerate(gene_names_sorted):
f.write(f'{g},{vae_data_sorted[i]}\n')
ora_dir
'../../data/results/3_node_significant_genes/functional/'
Here after running the AP functional R notebook we can visualise volcano plots of the results.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from adjustText import adjust_text
from sciviso import Vis
class Volcanoplot(Vis):
def __init__(self, df: pd.DataFrame, log_fc: str, p_val: str, label_column: str, title='',
xlabel='', ylabel='', invert=False, p_val_cutoff=0.05,
log_fc_cuttoff=2, label_big_sig=False, colours=None, offset=None,
text_colours=None, values_to_label=None, max_labels=10, values_colours=None,
figsize=(1.5, 1.5), title_font_size=8, label_font_size=6, title_font_weight=700):
super().__init__(df, figsize=figsize, title_font_size=title_font_size, label_font_size=label_font_size,
title_font_weight=title_font_weight)
super().__init__(df)
self.log_fc = log_fc
self.p_val = p_val
self.p_val_cutoff = p_val_cutoff
self.log_fc_cuttoff = log_fc_cuttoff
self.values_to_label = values_to_label
self.label_big_sig = label_big_sig
self.invert = invert
self.label_column = label_column
self.offset = offset
self.label = 'volcanoplot'
self.colours = {'ns_small-neg-logFC': 'lightgrey',
'ns_small-pos-logFC': 'lightgrey',
'ns_big-neg-logFC': 'grey',
'ns_big-pos-logFC': 'grey',
'sig_small-neg-logFC': 'lightgrey',
'sig_small-pos-logFC': 'lightgrey',
'sig_big-neg-logFC': '#df80ff',
'sig_big-pos-logFC': '#ffa366'} if colours is None else colours
self.xlabel = xlabel
self.ylabel = ylabel
self.title = title
self.figsize = figsize
self.max_labels = max_labels
self.values_colours = values_colours or {}
self.text_colours = text_colours or {}
def add_scatter_and_annotate(self, fig: plt, x_all: np.array, y_all: np.array,
colour: str, idxs: np.array, annotate=False, s=20):
x = x_all[idxs]
y = y_all[idxs]
ax = fig.scatter(x, y, c=colour, alpha=self.opacity, s=s, vmin=-10.0, vmax=10.0)
# Check if we want to annotate any of these with their gene IDs
if self.values_to_label is not None:
texts = []
labels = self.df[self.label_column].values[idxs]
for i, name in enumerate(labels):
if name in self.values_to_label:
lbl_bg = self.values_colours.get(name)
color = self.text_colours.get(name)
texts.append(fig.text(x[i], y[i], name, color=color, fontsize=6,
bbox=dict(fc="white", alpha=0.5, boxstyle='round,pad=0.1', lw=0)))
adjust_text(texts, force_text=2.0)
# Check if the user wants these labeled
if self.label_big_sig and annotate:
# If they do have a limit on the number of ones we show (i.e. we don't want 10000 gene names...)
max_values = -1 * self.max_labels
if len(y) < self.max_labels:
max_values = -1 * (len(y) - 1)
most_sig_idxs = np.argpartition(y, max_values)[max_values:]
labels = self.df[self.label_column].values[idxs][most_sig_idxs]
x = x[most_sig_idxs]
y = y[most_sig_idxs]
# We only label the ones with the max log fc
for i, name in enumerate(labels):
name = (' ').join(name.split('_')[1:]) #Format nicer
fig.annotate(name, (x[i], y[i]),
xytext=(0, 10),
textcoords='offset points', ha='center', va='bottom',
bbox=dict(boxstyle='round,pad=0.25',
fc=colour, alpha=0.2)
)
return ax
def plot(self):
"""
For annotation styling see: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.annotate
Returns
-------
"""
# if offset is not given, make the offset the smallest value in the dataset
if not self.offset:
vals = self.df[self.p_val].values
self.offset = np.min(vals[np.nonzero(vals)])
self.u.warn_p(['No offset was provided, setting offset to be smallest value recorded in dataset: ',
self.offset])
# x axis has log_fc, first only plot the values < cutoff
x = self.df[self.log_fc].values
y = -1 * np.log10(self.df[self.p_val].values + self.offset)
log_fc_np = self.df[self.log_fc].values
p_val_np = self.df[self.p_val].values
if self.invert:
x = -1 * np.log10(self.df[self.p_val].values + self.offset)
y = self.df[self.log_fc].values
sig_small_pos_logfc = np.where((p_val_np <= self.p_val_cutoff) & (np.abs(log_fc_np) < self.log_fc_cuttoff)
& (log_fc_np > 0))
sig_big_pos_logfc = np.where((p_val_np <= self.p_val_cutoff) & (np.abs(log_fc_np) >= self.log_fc_cuttoff)
& (log_fc_np > 0))
sig_small_neg_logfc = np.where((p_val_np <= self.p_val_cutoff) & (np.abs(log_fc_np) < self.log_fc_cuttoff)
& (log_fc_np <= 0))
sig_big_neg_logfc = np.where((p_val_np <= self.p_val_cutoff) & (np.abs(log_fc_np) >= self.log_fc_cuttoff)
& (log_fc_np <= 0))
# Plot the points
fig, ax = plt.subplots(figsize=self.figsize)
self.add_scatter_and_annotate(ax, x, y, self.colours['sig_small-pos-logFC'], sig_small_pos_logfc)
self.add_scatter_and_annotate(ax, x, y, self.colours['sig_big-pos-logFC'],
sig_big_pos_logfc, annotate=True, s=40)
# Negative
self.add_scatter_and_annotate(ax, x, y, self.colours['sig_small-neg-logFC'], sig_small_neg_logfc)
self.add_scatter_and_annotate(ax, x, y, self.colours['sig_big-neg-logFC'], sig_big_neg_logfc, annotate=True, s=40)
self.add_labels()
ax.tick_params(labelsize=6)
ax.tick_params(direction='out', length=2, width=0.5)
ax.spines['bottom'].set_linewidth(0.5)
ax.spines['top'].set_linewidth(0)
ax.spines['left'].set_linewidth(0.5)
ax.spines['right'].set_linewidth(0)
ax.tick_params(labelsize=6)
ax.tick_params(axis='x', which='major', pad=0)
ax.tick_params(axis='y', which='major', pad=0)
return ax
gsea_dir = '../../figures/3_node_significant_genes/gsea/'
for f in os.listdir(gsea_dir):
if 'GSEA' in f and 'KEGG' in f:
gsea_out = pd.read_csv(gsea_dir + f)
print(f)
volcanoplot = Volcanoplot(gsea_out, 'NES', 'padj', 'pathway',
f.split("_")[1], 'NES', '-log10(p adj)',
p_val_cutoff=1.0, max_labels=5,
label_big_sig=True, log_fc_cuttoff=1.5, figsize=(2.0,2.0))
sns.set_style("ticks")
volcanoplot.plot()
save_fig(f'Volcano_gsea_{f.split("_")[1]}_{f.split("_")[2]}')
plt.show()
def save_fig(title, ending='.svg'):
plt.savefig(f'{fig_dir}{title.replace(" ", "-")}{ending}')
for f in os.listdir(gsea_dir):
if 'Go' in f and 'svg' not in f:
gsea_out = pd.read_csv(gsea_dir + f)
gsea_out = gsea_out.fillna(1.0)
volcanoplot = Volcanoplot(gsea_out, 'NES', 'padj', 'pathway',
f.split("_")[1], 'NES', '-log10(p adj)',
p_val_cutoff=1.0, max_labels=5,
label_big_sig=True, log_fc_cuttoff=1.5, figsize=(2,2))
sns.set_style("ticks")
volcanoplot.plot()
save_fig(f'Volcano_gsea_GO_{f.split("_")[1]}_{f.split("_")[2]}', ending='.pdf' )
plt.show()